Original Article Qinan Wang, M.S.
Lyle School of Engineering, Southern Methodist University, Dallas, TX, 75275,
USA<EMAIL_ADDRESS>
# Intraday trading strategy based on time series and machine learning for
Chinese stock market

Qinan Wang  Lyle School of Engineering, Southern Methodist University, Dallas,
TX, 75275, USA  Yaomu Zhou  Lyle School of Engineering, Southern Methodist
University, Dallas, TX, 75275, USA  Junhao Shen  Lyle School of Engineering,
Southern Methodist University, Dallas, TX, 75275, USA

###### Abstract

This article comes up with an intraday trading strategy under T+1 using
Markowitz optimization and Multilayer Perceptron (MLP) with published stock
data obtained from the Shenzhen Stock Exchange and Shanghai Stock Exchange.
The empirical results reveal the profitability of Markowitz portfolio
optimization and validate the intraday stock price prediction using MLP. The
findings further combine the Mark- owitz optimization, an MLP with the trading
strategy, to clarify this strategy’s feasibility.

###### keywords:

Stock price, Prediction, Markowitz optimization, Multilayer Perceptron,
Validation

##  1 Introduction

The development of The Chinese stock market (A shares) since its inception has
been impressive. Especially in recent years, the Chinese stock market has been
strong, which seems to sign a bull market. Most of these companies have
outperformed expectations and performed well. Except for a few losers, all
sectors have performed strongly. It raises attention worldwide. We find that
it is invaluable to research quantitative methods for the stock market under
this condition.

In China’s stock market, the regulator implements T+1. That is, stocks can
only be sold the next day after they are bought on the same day. It is hard to
figure out a way of daily trading in the market. However, the price of a stock
fluctuates every day. The maximum amplitude of a single stock can even reach
20% (the daily cap and collar volatility difference: upper limit is 10%, lower
limit is -10%). Given this circumstance, we can carry out the following
operations: Open positions in one day advance. On the following day, sell the
stocks at a high intraday price, buy the same quantity of positions as selling
before at a low price of the day; Or buy at a low price and sell at a high
price. At this point, we have managed a similar T+0 operation. We make a
profit by the difference in price within the day. During this process, the
stock account share will not change, but the amount of money available in the
account will increase.

In previous publications, the stock price prediction has been researched in
many models. Tang et al. compared their model of autoregressive moving average
generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) with
the finite mixture of autoregressive (AR), a finite mixture of the
autoregressive moving average (ARMA) and a finite mixture of autoregressive
generalized autoregressive conditional heteroscedasticity (AR-GARCH) models
for finance exchange rate prediction. The experimental simulation indicated
that the mixture of the ARMA-GARCH model derives a GEM algorithm with better
performance than others. [1] In 2007, Ince et al. utilized technical
indicators with heuristic models, kernel principal component analysis, and
factor analysis to select the most useful inputs for a predicting model. They
used different inputs on multilayer perceptron (MLP) networks and support
vector regression (SVR). SVR and MLP networks required those different inputs
studied on comparison studies. Furthermore, proposed heuristic models produced
better results than the data mining method. Besides, there was no difference
between MLP and SVR techniques on their mean square error values. [2] Gong et
al. created a new method with Logistic Regression to forecast the next month’s
stock price trend basing on the current month in 2009. They chose Shenzhen
Development stock A(SDSA) from RESSET Financial Research Database as a study
case. Comparing with other models, e.g., RBF-ANN forecasting model, their
model was less complicated and at least 83% accurate in prediction. [3] In
2010, Budiman et al. used the Artificial Neural Network (ANN) and ARIMA method
to predict the results of ANTM (PT. Aneka Tambang) of stock. The study showed
that using the ANN method had a smaller error than the ARIMA approach. [4]
After three years, Alkhatib et al. forecasted stock price for a sample of six
companies on the Jordanian stock exchange utilizing the K- nearest neighbor
algorithm and nonlinear regression method to help investors, management,
decision-maker, and users make an accurate decision. Basing on the predicting
result, which is rational and reasonable, the KNN method was excellent with
the smallest error. Compared with the real stock price data, the forecasting
result and the actual stock price were almost parallel. [5] In 2016, Persio et
al. predicted stock price by using the Artificial Neural Network approach.
They also considered MLP, CNN, LSTM recurrent neural networks techniques, and
S&P500 historical time series to predict the stock price trend. As a result,
they indicated that neural networks could forecast future movements’ financial
time series and propose more ways to improve the results. [6] In recent years,
there is some research on predicting stock price by deep learning method with
algorithm development. For example, in 2019, Nikou et al. studied to assess
the forecasting power by Machine Learning models in a stock market. They
created four MLA models to make predictions. The results showed that the deep
learning approach was best than others. The second-best method was supports
vector regression concerning neural network and random forest methods with
less error. [7]

Most of the researches are only for price prediction since the T+1 regulation
of China’s stock market makes intraday trading difficult. It is innovative to
research the intraday trading strategy for China’s stock market. Besides,
Guresen et al. provided a detailed survey for 25 articles of the NNs
application on the finance and applied a multi-layer perceptron model, a
dynamic artificial neural network and hybrid networks to predict NASDAQ Stock
Exchange Index. They concluded that NN-based solutions outperform other
statistical approaches in most cases. [8] Hence, we combine the prediction by
multilayer perceptron (MLP) and practical operation to design the strategy.
This strategy is validated by simulated trade.

In this strategy, to assure both the profit of origin holdings and intraday
trading, we select a portfolio consists of promising stocks, and manage risk.
To avoid unexpected volatility of certain industries or companies, we get
three stocks from 10 industries, respectively. These industries are Science
and Technology, Basis Materials, Consumption (periodic), Finance, Service,
Means of Production, Energy, Consumption (aperiodic), Health Care,
transportation. In each industry, we use the top three performance stocks of
the last month.On the first day of trading, we open positions for all stocks
in the portfolio. On the following days, we trade two times each day to gain
from the intraday volatility. The daily trading hours for continuous bidding
of A shares are from 9:30 to 11:30 in the morning and from 13:00 to 15:00 in
the afternoon. The trading hours are only 4 hours a day, and there is a
1.5-hour break at noon. We use one-minute trading data from 9:30 to 11:20 to
train the multilayer perceptron (MLP). This model is used to predict the stock
price of 13:00 to 14:50. With the price point at 14:50, we make trading
decision by the difference between this point and price at 11:20. If the
prediction price is lower than the price at 11:20, we sell holding positions
before the close in the morning and buy back at 14:50. If the prediction price
is higher than the price at 11:20, we buy additional positions before the
close in the morning and sell them at 14:50. The model is retrained every day
for a possible large gap between successive days. The goal of this article is
to gain profit by intraday trend prediction.

In the next section, we describe the data we used in the analyses. The Method
section contains Markowitz portfolio optimization, multi-layer perceptron
(MLP), daily volatility, and measurement of return. The Results section
includes the results of portfolio optimization, modelling, and an evaluation
of the return of the strategy. The last section discusses the model
performance, further improvement, and comparison with other models.

##  2 Data

Figure 1: Histogram of daily volatility

Given the previous performance in 2020, we pick 30 stocks for the portfolio.
This study’s experimental data include close price of minutes data in October
and close price of daily data from January 2020 to September 2020. We
collected the data from Xueqiu Finance. Daily data is used in Markowitz
Optimization. Minutes data is used for day trade. The data plots are as Figure
4 and Figure 5 (See Appendix). The tickers information is as Table 3 (See
Appendix). In Figure 1, the daily volatility ranges from 0 to 0.03. We have a
0.6 probability that the daily volatility is over 0.002. It shows a potential
earning for the day trade strategy.

##  3 Method

###  3.1 Markowitz portfolio optimization

####  3.1.1 Model

Markowitz’s portfolio theory was invented in 1952. He proposed the portfolio
optimization model using the mean and variance of individual stock returns in
an asset portfolio to find the Efficient Frontier of an investment portfolio,
the portfolio with the lowest variance under a certain yield level. According
to Markowitz’s portfolio model, it is necessary to invest in diverse stocks
and ensure that stocks’ correlation coefficient is low to minimize a
portfolio’s risk. From the relationship between the return and risk of risky
assets, it discusses selecting the economic system’s optimal asset portfolio.
The portfolio selection model focuses on risk diversification and
quantitatively determine the portfolio.

The apparent form of parametric optimization of Markowitz model has the
following mathematical representation:

$\begin{array}[]{l}\max\mathbb{E}\left(r_{P}\right)=\max\sum_{i=1}^{n}\omega_{i}\mu_{i}\\\
\min\sigma_{P}=\min\sqrt{\sum_{i=1}^{n}\sum_{j=1}^{n}\omega_{i}\omega_{j}\sigma_{ij}}\\\
0\leq\omega_{i}\leq 1,\quad i=1,\ldots,n\\\
\sum_{i=1}^{n}\omega_{i}=1\end{array}$

where $\omega_{i}$ is the percentage of capital that will be invested in asset
$i;r^{i}$ is the return on asset $i;\mu_{i}$ the expected return on asset
$i;\mu_{ij}$ is the covariance between the return on assets $i$ and
$j;\mathbb{E}\left(r_{P}\right)$ is the expected return of the portfolio;
$\sigma_{P}$ is the risk of the portfolio.[9] The Markowitz model is based on
several assumptions concerning the behavior of investors and financial
markets:[10]

  * •

A probability distribution of possible returns over some holding period can be
estimated by investors.

  * •

Investors have single-period utility functions in which they maximize utility
within the framework of diminishing marginal utility of wealth.

  * •

Variability about the possible values of return is used by investors to
measure risk.

  * •

Investors care only about the means and variance of the returns of their
portfolios over a particular period.

  * •

Expected return and risk as used by investors are measured by the first two
moments of the probability distribution of returns-expected value and
variance.

  * •

Return is desirable; risk is to be avoided.

  * •

Financial markets are frictionless.

####  3.1.2 Sharpe ratio

The Sharpe ratio can be used to measure the return on investment at a given
risk. This ratio adjusts the return on investment, allowing us to compare
different investments’ performance at a given scale of risk. Without the
limitation of scale risk, we cannot compare the returns and risk performance
of different securities portfolios.

$\text{ Sharpe Ratio }=\frac{R_{p}-R_{rf}}{\sigma_{p}}$

where $R_{p}$ is the expected portfolio/asset return, $R_{rf}$ is the risk-
free rate of return, $\sigma_{p}$ is portfolio/asset standard deviation.

###  3.2 Multilayer Perceptron (MLP)

The architecture of artificial neural networks (ANNs) is based on connections
of layers by nodes called neurons as well as the biological neurons of brain.
[11] Each path transmits a signal among neurons in a manner similar to that of
synapses. [12] MLP, as a feedforward ANN, contains three main parts: one input
layer, one or more hidden layers and one output layer, which can be
successfully employed for prediction, classification, signal processing and
error filtering. [13] Each node employs one nonlinear function. MLP employs
backpropagation learning algorithm for training process. [14, 15] MLP as
popular and frequently used techniques among other MLPs was employed to
predict the direction value. Figure 2 indicates the architecture of developed
network. [16]

Figure 2: Multilayer Perceptron structure

###  3.3 Daily volatility

To verify the strategy feasibility, we need the probability mass function
(pmf) of the volatility. Instead of represented by the standard deviation, the
volatility is defined as the formula:

$\sigma=\frac{|p_{a}-p_{b}|}{p_{a}}$

where $p_{a}$ is the price of the stock at 11:20, $p_{b}$ is the price of the
stock at 14:50, $\sigma$ is the daily volatility.

###  3.4 Measurement of return

####  3.4.1 Monthly return

In this strategy, we will have origin positions for 30 stocks. The monthly
return for each stock is defined as the formula:

$r_{m}=\frac{p_{e}-p_{s}}{p_{s}}$

where $r_{m}$ is the monthly return of the stock; $p_{e}$ is the selling
price; $p_{s}$ is the buying price.

####  3.4.2 Daily return

In this strategy, we will trade a stock two times per day, selling in the
morning and buying in the afternoon, vice versa. We assume that trading the
same amount as origin position each day. Given any stock and its daily buying,
daily selling price, origin position price, we have the daily return of the
stock as the formula:

$r_{t}=\frac{s_{t}-b_{t}}{o}$

where $r_{t}$ is the return of the stock at time $t$, $s_{t}$ is the selling
price at time $t$, $b_{t}$ is the buying price of the stock at time $t$, $o$
is the origin position price.

####  3.4.3 Yearly return

Given any stock and its daily or monthly return, the formulas of expected
yearly return for this stock are:

$\mathbb{E}\left(r_{y}\right)=(1+\mathbb{E}\left(r_{m}\right))^{12}-1$
$\mathbb{E}\left(r_{y}\right)=(1+\sum_{t=1}^{n}\mathbb{E}\left(r_{t}\right))^{12}-1$

where $r_{y}$ is the yearly return, $r_{m}$ is the monthly return, $r_{t}$ is
the daily return, $n$ is trading days of that month, $\mathbb{E}(\cdot)$ is
the expectation function.

####  3.4.4 Portfolio return

Given any set of risky assets and a set of weights that describe how the
portfolio investment is split, the general formula of expected return for
$\mathrm{n}$ assets is:

$\mathbb{E}\left(r_{P}\right)=\sum_{i=1}^{n}w_{i}\mathbb{E}\left(r_{i}\right)$

where $\sum_{i=1}^{n}w_{i}=1.0$; $n=$ the number of securities; $w_{i}$ is the
proportion of the funds invested in security $i$; $r_{i}$ and $r_{P}$ are the
return on ith security and portfolio $p$; $\mathbb{E}(\cdot)$ is the
expectation function.

The return computation is nothing more than finding the weighted average
return of the securities included in the portfolio.[10]

##  4 Result

###  4.1 Portfolio optimization

For the constraints of the quadratic programming optimization, the first
constraint is that the sum of the weights equals 1, the second constraint is
that the weight is greater than or equal to 0 (not allowed to sell short). The
stock returns and standard deviation are calculated from the daily close price
from January to September 2020. Considering different investment risk
preferences, we found two optimal weights with the greatest Sharpe ratio and
the smallest standard deviation. To calculate the Sharpe ratio, set the risk-
free interest rate as 4%.

Figure 3: Expected returns and Sharpe ratios for random portfolios.
Red rhombus is the optimal portfolio with the greatest Sharpe ratio. Blue
rhombus is the optimal portfolio with the smallest standard deviation.

In Figure 3, the points are randomly produced 50,000 groups of weights for the
30 stocks. The optimal portfolio with the greatest Sharpe ratio has an
expected yearly return of 65.44% and a yearly volatility 0.27. The Sharpe
ratio of this portfolio is 0.51. The optimal portfolio with the smallest
standard deviation has an expected yearly return of 17.95% and a yearly
volatility 0.21. In the following research, we use the greatest Sharpe ratio
portfolio for a high return. The Table 1 shows the weights of each stock in
this optimal portfolio.

As the result of optimization, the greatest weight is 0.0818 for ’SZ300122’
and the smallest weight is 0.0049 for ’SZ000725’. The two stocks have monthly
returns 14.98% and -3.67% in October respectively. The greater weights are
given to ’SZ002475’, ’SZ002594’, ’SZ002607’, ’SZ300122’, ’SH600519’,
’SH601888’, ’SH601899’, ’SH603288’, in which the weights are over 0.05. In
order of the stocks in the Table 1 from top to bottom, they provide -0.02%,
0.27%, 0.12%, -0.02%, 0.45%, 0.02%, 0.61%, -0.27%, 2.75%, 1.46%, 0.86%, 1.23%,
-0.04%, -0.01%, 0.84%, 0.02%, -0.09%, -0.15%, -0.01%, -0.03%, -0.05%, -0.02%,
-0.05%, 0, 0.01%, 0.01%, 0.76%, -0.70%, -0.02%, 0.08% respectively to the
portfolio monthly return. Given the contributions to the portfolio, the top-
performing stocks are ’SZ002594’, ’SZ002607’, ’SZ300015’, among which
contributions to the portfolio are over 1% monthly.

By the optimal portfolio with the greatest Sharpe ratio, we have a monthly
return of 8.02% in October and a yearly return of 152.35% for the origin
holdings.

Table 1: 30 stocks October daily return Ticker symbol | Weight | Close price on 2020-9-30 | Close price on 2020-10-30 | Monthly return (%)
---|---|---|---|---
SZ000002 | 0.0110 | 28.02 | 27.49 | -1.89
SZ000333 | 0.0373 | 72.60 | 77.87 | 7.26
SZ000651 | 0.0128 | 53.30 | 58.43 | 9.62
SZ000725 | 0.0049 | 4.91 | 4.73 | -3.67
SZ000858 | 0.0425 | 221.00 | 244.35 | 10.57
SZ002352 | 0.0091 | 81.20 | 82.80 | 1.97
SZ002415 | 0.0345 | 38.11 | 44.90 | 17.82
SZ002475 | 0.0673 | 57.13 | 54.86 | -3.97
SZ002594 | 0.0733 | 116.24 | 159.81 | 37.48
SZ002607 | 0.0692 | 32.63 | 39.52 | 21.12
SZ300015 | 0.0409 | 51.42 | 62.26 | 21.08
SZ300122 | 0.0818 | 139.31 | 160.18 | 14.98
SH600009 | 0.0090 | 68.78 | 66.10 | -3.90
SH600028 | 0.0058 | 3.91 | 3.90 | -2.56
SH600104 | 0.0348 | 19.13 | 23.15 | 24.01
SH600276 | 0.0226 | 89.82 | 88.84 | 1.09
SH600309 | 0.0068 | 69.30 | 78.49 | -13.26
SH600346 | 0.0368 | 18.56 | 19.30 | -3.99
SH600519 | 0.0782 | 1668.50 | 1670.02 | -0.09
SH601012 | 0.0200 | 75.01 | 75.99 | -1.31
SH601088 | 0.0435 | 16.47 | 16.65 | -1.09
SH601225 | 0.0047 | 8.39 | 8.75 | -4.29
SH601318 | 0.0259 | 76.26 | 77.83 | -2.06
SH601398 | 0.0140 | 4.92 | 4.92 | 0
SH601766 | 0.0060 | 5.49 | 5.39 | 1.82
SH601857 | 0.0075 | 4.11 | 4.07 | 0.97
SH601888 | 0.0719 | 222.94 | 199.40 | 10.56
SH601899 | 0.0513 | 6.15 | 6.99 | -13.66
SH601939 | 0.0094 | 6.15 | 6.29 | -2.28
SH603288 | 0.0672 | 162.10 | 160.20 | 1.17

###  4.2 Model

To predict by MLP, we trained a new model for each trading day using the data
before 11:20. For each model, lags of input time series to use as inputs are
60. The number of networks to train is 100, and the result is the ensemble
forecast. To avoid the affection of outliers, the combination operator for
forecasts is ’median.’ The best number of hidden nodes is found by 5-fold
cross-validation.

We use this prediction model to do paper trading. This model is used to
predict the stock price of 13:00 to 14:50. With the price point at 14:50, we
make trading decision by the difference between this point and the price at
11:20. If the prediction price is lower than the price at 11:20, we sell
holding positions before the close in the morning and buy back at 14:50. If
the prediction price is higher than the price at 11:20, we buy additional
positions before the close in the morning and sell them at 14:50. Given the
trading fee of 0.05%, if the difference between the predicted price at 14:50
and the price at 11:20 is not more than 0.1% of the latter, we will not trade
on that day. To calculate the daily return, we use the close price on
September 30th as origin position price. The October sum of daily return for
each stock in the portfolio is as the Table 2. The specific earnings and model
prediction for each day of each stock are shown in Appendix.

In Table 2, ’SZ000333’, ’SZ000651’, ’SZ000725’, ’SZ000858’, ’SZ002352’,
’SZ002415’, ’SZ002475’, ’SZ002594’, ’SZ002 607’, ’SZ300015’, ’SH600028’,
’SH600309’, ’SH600346’, ’SH601088’, ’SH601398’, ’SH601857’, ’SH603288’ have
positive returns. The number of positive returns stocks is 17 and the number
of negative returns sto cks is 13. The top-performing stocks in the intraday
trading strategy are ’SZ002594’, ’SZ002607’ and ’SZ002475’ with the sum of
daily return of 14.13%, 10.10%, and 7.09% respectively. Particularly, they are
given greater weights of 0.0733, 0.0692, and 0.0673.

For ’SZ002594’, in the Table 12 (See Appendix), the highest daily return is
6.27% and the lowest return is -5.43%. For ’SZ002607’, in the Table 13 (See
Appendix), the highest daily return is 3.88% and the lowest return is -2.21%.
For ’SZ002478’, in the Table 11 (See Appendix), the highest daily return is
2.94% and the lowest return is -0.68%. All three stocks have posted strong
gains throughout the month, but have also posted big losses in some trading
sessions.

Stocks with high intraday volatility, such as ’SZ300122’, ’SH600104’ and
’SH601318’ are prone to large losses. The October sum of daily returns for the
three stocks are -11.48%, -7.11% and -4.04%, respectively. The weights for
these three stocks are 0.0818, 0.0348, 0.0259.

For ’SZ300122’, in the Table 15 (See Appendix), the highest daily return is
8.48% and the lowest return is -7.90%. For ’SH600104’, in the Table 18 (See
Appendix), the highest daily return is 1.05% and the lowest return is -3.08%.
For ’SH601318’, in the Table 26 (See Appendix), the highest daily return is
1.08% and the lowest return is -1.32%. All three stocks have posted strong
gains throughout the month, but have also posted big losses in some trading
sessions.

In the prediction, the model is able to predict accurately when price rises or
falls periodically. But in periods of sustained rise or fall, when models
typically predict reversion to the mean, it tends to generate large trading
losses.

Combining the prediction model with the intraday trading strategy, we have a
return of 1.41% in October and a yearly return of 18.24%.

Table 2: 30 stocks October daily return Ticker symbol | October sum of daily return (%)
---|---
SZ000002 | -1.96
SZ000333 | 3.66
SZ000651 | 2.35
SZ000725 | 1.22
SZ000858 | 0.88
SZ002352 | 2.38
SZ002415 | 0.25
SZ002475 | 7.09
SZ002594 | 14.13
SZ002607 | 10.10
SZ300015 | 2.40
SZ300122 | -11.48
SH600009 | -1.45
SH600028 | 1.29
SH600104 | -7.11
SH600276 | -1.51
SH600309 | 5.25
SH600346 | 6.74
SH600519 | -3.41
SH601012 | -1.15
SH601088 | 0.14
SH601225 | -0.7
SH601318 | -4.04
SH601398 | 0.61
SH601766 | -0.19
SH601857 | 1.69
SH601888 | -0.72
SH601899 | -1.12
SH601939 | -1.78
SH603288 | 4.72

###  4.3 Portfolio return

Total portfolio returns are divided into origin position gains and intraday
trading gains. As mentioned before, the origin position gains for October is
8.02% and the intraday trading gains for October is 1.41%. The total portfolio
returns for October is 9.43% and the yearly return is 194.87%.

##  5 Discussion

The current study’s purpose was to combine the prediction model and practical
intraday trading strategy under the T+1 constraint in China stock market. This
study has shown that the prediction model and strategy is profitable for
trading. The findings will be of interest to the Fund company investing in
China stock market and interested in the intraday trading under T+1.

More broadly, research is also needed to determine some practical trading
problems. The first problem is to set a stop loss. When we find the prediction
trend is reverse to the actual trend and the loss is too large in a day, like
3%, we should trade ahead of time to avoid a larger loss. The second problem
is to set a higher threshold for the decision of trading. Because the trading
fee is 0.05% per time, it will cause loss if we trade too many times, even the
strategy is profitable. In this article, we set the threshold as 0.1%. If the
difference between the prediction price at 14:50 and the real price at 11:20
is not more than or less than 0.1% of the origin position price, we will not
trade on that day. To reduce the trading fee, we can increase the threshold to
0.2% or more.

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## Appendix

Table 3: Ticker symbol reference Number of Stock | Ticker symbol | Company
---|---|---
01 | SZ000002 | CHINA VANKE CO., Ltd.
02 | SZ000333 | Midea Group
03 | SZ000651 | Gree Electric Appliances Inc. of Zhuhai
04 | SZ000725 | Boe Technology Group Co., Ltd.
05 | SZ000858 | Wuliangye Yibin Co.,Ltd.
06 | SZ002352 | SF Express (Group) Co., Ltd.
07 | SZ002415 | Hangzhou Hikvision Digital Technology Co., Ltd.
08 | SZ002475 | Shenzhen Luxshare Precision Industry Co.,Ltd.
09 | SZ002594 | BYD Co., Ltd.
10 | SZ002607 | Offcn Education Technology Co., Ltd.
11 | SZ300015 | Aier Eye Hospital Group Co., Ltd.
12 | SZ300122 | Chongqing Zhifei Biological Products Co., Ltd.
13 | SH600009 | Shanghai Airport Authority
14 | SH600028 | Sinopec, the China Petroleum and Chemical Corporation
15 | SH600104 | SAIC Motor Corporation Limited
16 | SH600276 | Jiangsu Hengrui Medicine Co., Ltd.
17 | SH600309 | Wanhua Chemical Group Co., Ltd.
18 | SH600346 | Hengli Group
19 | SH600519 | Kweichow Moutai Co., Ltd.
20 | SH601012 | LONGi Green Energy Technology Co.,Ltd.
21 | SH601088 | China Shenhua Energy Co., Ltd.
22 | SH601225 | Shaanxi Coal and Chemical Industry Group Co., Ltd.
23 | SH601318 | Ping An Insurance (Group) Company of China, Ltd.
24 | SH601398 | Industrial and Commercial Bank of China Limited
25 | SH601766 | CRRC Corporation Limited
26 | SH601857 | China National Petroleum Corporation
27 | SH601888 | China Tourism Group Duty Free Corporation Limited.
28 | SH601899 | Zijin Mining Group Co., Limited
29 | SH601939 | China Construction Bank Corporation
30 | SH603288 | Foshan Haitian Flavouring & Food Co. Ltd
Figure 4: Minute close price on 2020-10-09 Figure 5: Daily close price from January to September 2020 Table 4: SZ000002 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 27.90 | 27.90 | 27.89 | 0
2020-10-12 | 28.37 | 28.26 | 28.39 | 0
2020-10-13 | 27.90 | 27.95 | 27.75 | -0.18
2020-10-14 | 27.75 | 27.79 | 27.49 | -0.14
2020-10-15 | 27.77 | 27.73 | 27.80 | -0.14
2020-10-16 | 27.81 | 27.83 | 28.20 | 0.07
2020-10-19 | 27.96 | 27.74 | 28.11 | -0.79
2020-10-20 | 27.42 | 27.45 | 27.44 | 0
2020-10-21 | 27.35 | 27.60 | 27.22 | -0.89
2020-10-22 | 27.79 | 27.99 | 27.96 | 0.71
2020-10-23 | 27.93 | 27.95 | 27.97 | 0.07
2020-10-26 | 27.99 | 27.99 | 27.96 | 0
2020-10-27 | 27.61 | 27.50 | 27.69 | -0.39
2020-10-28 | 27.00 | 26.98 | 26.98 | 0
2020-10-29 | 27.09 | 27.45 | 27.24 | 1.28
2020-10-30 | 27.88 | 27.44 | 28.36 | -1.57
Table 5: SZ000333 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 73.88 | 73.38 | 73.96 | -0.69
2020-10-12 | 73.16 | 73.70 | 73.15 | 0
2020-10-13 | 74.90 | 75.91 | 74.92 | 0
2020-10-14 | 75.27 | 75.41 | 75.32 | 0
2020-10-15 | 76.73 | 76.29 | 76.54 | 0.6
2020-10-16 | 74.73 | 75.10 | 75.02 | 0.51
2020-10-19 | 75.12 | 74.84 | 74.41 | 0.39
2020-10-20 | 76.03 | 76.19 | 75.82 | -0.22
2020-10-21 | 77.45 | 77.31 | 79.25 | -0.19
2020-10-22 | 77.46 | 78.22 | 77.35 | -1.05
2020-10-23 | 77.36 | 75.64 | 77.44 | 0
2020-10-26 | 76.37 | 76.53 | 76.91 | 0.22
2020-10-27 | 75.95 | 75.99 | 75.91 | 0
2020-10-28 | 76.48 | 76.89 | 74.73 | -0.56
2020-10-29 | 79.78 | 81.64 | 82.51 | 2.56
2020-10-30 | 79.28 | 77.76 | 77.31 | 2.09
Table 6: SZ000651 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 54.99 | 54.65 | 55.02 | 0
2020-10-12 | 55.77 | 55.77 | 55.70 | 0
2020-10-13 | 55.76 | 55.89 | 55.66 | -0.24
2020-10-14 | 57.54 | 57.64 | 57.67 | 0.19
2020-10-15 | 58.37 | 57.91 | 58.37 | 0
2020-10-16 | 58.21 | 57.73 | 58.33 | -0.90
2020-10-19 | 57.81 | 57.25 | 57.63 | 1.05
2020-10-20 | 57.56 | 57.64 | 57.72 | 0.15
2020-10-21 | 57.87 | 57.92 | 57.88 | 0
2020-10-22 | 58.35 | 58.56 | 58.33 | 0
2020-10-23 | 58.28 | 58.51 | 57.61 | -0.43
2020-10-26 | 58.00 | 58.10 | 57.28 | -0.19
2020-10-27 | 57.63 | 57.34 | 57.73 | -0.54
2020-10-28 | 57.26 | 57.31 | 57.39 | 0.09
2020-10-29 | 57.28 | 58.12 | 57.92 | 1.58
2020-10-30 | 59.30 | 58.45 | 59.22 | 1.59
Table 7: SZ000725 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 5.13 | 5.18 | 5.27 | 1.02
2020-10-12 | 5.24 | 5.23 | 5.23 | 0.20
2020-10-13 | 5.16 | 5.17 | 5.15 | -0.20
2020-10-14 | 5.06 | 5.05 | 5.12 | -0.20
2020-10-15 | 5.00 | 4.97 | 5.01 | -0.61
2020-10-16 | 4.89 | 4.91 | 4.88 | -0.41
2020-10-19 | 4.88 | 4.85 | 4.88 | 0
2020-10-20 | 4.79 | 4.92 | 4.80 | 2.65
2020-10-21 | 4.80 | 4.85 | 4.81 | 1.02
2020-10-22 | 4.89 | 4.88 | 4.90 | -0.20
2020-10-23 | 4.83 | 4.79 | 4.83 | 0
2020-10-26 | 4.79 | 4.79 | 4.80 | 0
2020-10-27 | 4.71 | 4.74 | 4.70 | -0.61
2020-10-28 | 4.72 | 4.79 | 4.70 | -1.43
2020-10-29 | 4.87 | 4.83 | 5.13 | -0.81
2020-10-30 | 4.87 | 4.83 | 5.13 | -0.81
Table 8: SZ000858 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 229.23 | 226.94 | 234.61 | -1.04
2020-10-12 | 237.99 | 241.99 | 245.37 | 1.81
2020-10-13 | 241.61 | 242.52 | 241.61 | 0
2020-10-14 | 239.39 | 239.61 | 228.42 | -0.09
2020-10-15 | 238.81 | 238.85 | 238.82 | 0
2020-10-16 | 237.12 | 236.93 | 238.11 | -0.08
2020-10-19 | 236.27 | 235.69 | 237.12 | -0.26
2020-10-20 | 239.20 | 240.98 | 239.53 | 0.81
2020-10-21 | 240.94 | 241.86 | 241.02 | 0
2020-10-22 | 240.30 | 242.61 | 240.70 | 1.05
2020-10-23 | 240.66 | 237.10 | 241.41 | -1.61
2020-10-26 | 233.87 | 233.17 | 235.84 | -0.32
2020-10-27 | 232.54 | 234.10 | 232.51 | 0
2020-10-28 | 239.09 | 240.40 | 243.81 | 0.59
2020-10-29 | 253.13 | 251.03 | 266.74 | -0.95
2020-10-30 | 254.94 | 243.75 | 245351 | 0.99
Table 9: SZ002352 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 86.20 | 85.37 | 87.05 | -1.02
2020-10-12 | 86.40 | 86.91 | 85.96 | -0.63
2020-10-13 | 89.97 | 92.26 | 89.54 | -2.82
2020-10-14 | 92.07 | 92.82 | 92.44 | 0.92
2020-10-15 | 89.75 | 90.3 | 88.93 | -0.68
2020-10-16 | 89.30 | 89.66 | 89.43 | 0.44
2020-10-19 | 89.59 | 89.37 | 89.98 | -0.27
2020-10-20 | 88.48 | 90.60 | 88.59 | 2.61
2020-10-21 | 89.44 | 89.50 | 89.33 | -0.07
2020-10-22 | 86.37 | 88.01 | 86.06 | -2.02
2020-10-23 | 86.49 | 85.02 | 84.93 | 1.81
2020-10-26 | 87.98 | 87.17 | 87.82 | 1.00
2020-10-27 | 86.14 | 87.04 | 86.17 | 0
2020-10-28 | 86.18 | 86.82 | 85.95 | -0.79
2020-10-29 | 85.54 | 86.33 | 85.78 | 0.97
2020-10-30 | 84.88 | 82.50 | 83.42 | 2.93
Table 10: SZ002415 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 38.19 | 38.10 | 38.01 | 0.24
2020-10-12 | 37.96 | 38.65 | 37.96 | 0
2020-10-13 | 39.01 | 39.03 | 38.75 | -0.05
2020-10-14 | 38.80 | 38.97 | 39.04 | 0.45
2020-10-15 | 39.63 | 39.43 | 39.55 | 0.52
2020-10-16 | 38.45 | 38.52 | 38.67 | 0.18
2020-10-19 | 38.98 | 39.26 | 39.04 | 0.73
2020-10-20 | 39.18 | 39.19 | 39.15 | 0
2020-10-21 | 39.39 | 39.20 | 39.32 | 0.5
2020-10-22 | 39.39 | 39.24 | 39.03 | 0.39
2020-10-23 | 40.24 | 39.00 | 40.34 | -3.25
2020-10-26 | 43.00 | 42.56 | 46.38 | -1.15
2020-10-27 | 44.00 | 44.09 | 45.94 | 0.24
2020-10-28 | 44.31 | 44.22 | 44.15 | 0.24
2020-10-29 | 44.55 | 45.01 | 44.72 | 1.21
2020-10-30 | 46.00 | 45.01 | 45.99 | 0
Table 11: SZ002475 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 61.13 | 61.36 | 61.49 | 0.40
2020-10-12 | 61.04 | 61.98 | 61.21 | 1.65
2020-10-13 | 61.75 | 61.34 | 61.08 | 0.72
2020-10-14 | 59.44 | 59.88 | 59.80 | 0.77
2020-10-15 | 60.25 | 59.92 | 60.01 | 0.58
2020-10-16 | 58.11 | 58.40 | 56.08 | -0.51
2020-10-19 | 58.91 | 58.49 | 58.96 | 0
2020-10-20 | 58.75 | 59.17 | 58.73 | 0
2020-10-21 | 57.62 | 57.74 | 57.62 | 0
2020-10-22 | 57.15 | 57.42 | 57.28 | 0.47
2020-10-23 | 57.20 | 56.78 | 57.40 | -0.74
2020-10-26 | 57.58 | 57.21 | 57.67 | -0.65
2020-10-27 | 56.56 | 57.50 | 56.70 | 1.65
2020-10-28 | 59.10 | 59.49 | 58.63 | -0.68
2020-10-29 | 58.71 | 58.43 | 58.42 | 0.49
2020-10-30 | 56.64 | 54.96 | 55.31 | 2.94
Table 12: SZ002594 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 123.68 | 120.00 | 123.56 | 0
2020-10-12 | 121.03 | 128.36 | 121.39 | 6.31
2020-10-13 | 129.20 | 129.29 | 128.69 | -0.08
2020-10-14 | 130.32 | 130.89 | 132.67 | 0.49
2020-10-15 | 134.38 | 131.94 | 134.27 | 0
2020-10-16 | 125.88 | 127.69 | 119.85 | -1.56
2020-10-19 | 127.25 | 127.10 | 127.43 | -0.13
2020-10-20 | 131.51 | 138.80 | 132.12 | 6.27
2020-10-21 | 137.09 | 138.00 | 136.99 | 0
2020-10-22 | 140.50 | 144.52 | 144.37 | 3.46
2020-10-23 | 141.00 | 137.52 | 142.19 | -2.99
2020-10-26 | 141.86 | 139.95 | 141.92 | 0
2020-10-27 | 137.85 | 140.37 | 137.88 | 0
2020-10-28 | 147.91 | 151.40 | 154.99 | 3
2020-10-29 | 158.02 | 163.59 | 159.63 | 4.79
2020-10-30 | 166.48 | 160.17 | 167.42 | -5.43
Table 13: SZ002607 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 33.07 | 32.51 | 32.95 | 1.72
2020-10-12 | 31.94 | 32.07 | 31.02 | -0.40
2020-10-13 | 32.41 | 32.94 | 32.87 | 1.62
2020-10-14 | 36.25 | 36.25 | 36.19 | 0
2020-10-15 | 36.32 | 36.69 | 35.98 | -1.13
2020-10-16 | 37.88 | 38.64 | 37.95 | 2.33
2020-10-19 | 37.70 | 38.17 | 37.76 | 1.44
2020-10-20 | 37.84 | 37.73 | 37.98 | -0.34
2020-10-21 | 38.08 | 38.09 | 37.99 | -0.03
2020-10-22 | 38.67 | 38.71 | 38.76 | 0.12
2020-10-23 | 39.31 | 37.89 | 39.27 | 4.35
2020-10-26 | 37.23 | 37.34 | 36.99 | -0.34
2020-10-27 | 38.10 | 38.06 | 38.17 | -0.12
2020-10-28 | 39.47 | 38.75 | 40.80 | -2.21
2020-10-29 | 39.87 | 39.62 | 41.18 | -0.77
2020-10-30 | 40.58 | 39.32 | 40.52 | 3.86
Table 14: SZ300015 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 52.54 | 53.28 | 52.53 | 0
2020-10-12 | 55.25 | 57.47 | 57.36 | 4.32
2020-10-13 | 56.80 | 58.12 | 54.84 | -2.57
2020-10-14 | 57.90 | 57.72 | 57.49 | 0.35
2020-10-15 | 57.15 | 57.16 | 56.24 | -0.02
2020-10-16 | 56.55 | 57.18 | 56.60 | 0
2020-10-19 | 56.40 | 56.20 | 56.78 | -0.39
2020-10-20 | 56.91 | 57.43 | 56.99 | 1.01
2020-10-21 | 57.67 | 58.25 | 57.44 | -1.13
2020-10-22 | 57.78 | 58.80 | 57.76 | 0
2020-10-23 | 58.58 | 56.96 | 58.65 | -3.15
2020-10-26 | 56.91 | 57.00 | 56.54 | -0.18
2020-10-27 | 60.30 | 61.50 | 60.95 | 2.33
2020-10-28 | 62.70 | 63.14 | 64.55 | 0.86
2020-10-29 | 62.75 | 62.58 | 62.57 | 0.33
2020-10-30 | 62.38 | 62.05 | 62.12 | 0.64
Table 15: SZ300122 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 148.41 | 149.98 | 150.60 | 1.13
2020-10-12 | 153.97 | 156.10 | 150.39 | -1.53
2020-10-13 | 163.38 | 156.41 | 167.50 | -5.00
2020-10-14 | 160.90 | 157.95 | 161.15 | -2.12
2020-10-15 | 154.10 | 156.55 | 145.62 | -1.76
2020-10-16 | 157.74 | 161.87 | 159.79 | 2.96
2020-10-19 | 159.57 | 158.60 | 159.76 | -0.70
2020-10-20 | 155.85 | 160.01 | 155.50 | -2.99
2020-10-21 | 164.12 | 162.61 | 164.03 | 0
2020-10-22 | 154.45 | 153.79 | 155.32 | -0.47
2020-10-23 | 153.80 | 141.99 | 153.26 | 8.48
2020-10-26 | 143.25 | 142.92 | 146.33 | -0.24
2020-10-27 | 143.78 | 146.30 | 143.25 | -1.81
2020-10-28 | 145.28 | 147.06 | 144.63 | -1.28
2020-10-29 | 151.00 | 162.00 | 147.61 | -7.90
2020-10-30 | 160.74 | 158.30 | 160.07 | 1.75
Table 16: SH600009 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 69.25 | 69.27 | 68.98 | -0.03
2020-10-12 | 69.89 | 69.91 | 69.81 | -0.03
2020-10-13 | 69.73 | 69.92 | 68.98 | -0.28
2020-10-14 | 69.19 | 70.38 | 69.17 | 0
2020-10-15 | 70.00 | 69.62 | 69.55 | 0.55
2020-10-16 | 69.55 | 69.50 | 69.75 | -0.07
2020-10-19 | 69.32 | 69.00 | 69.50 | -0.47
2020-10-20 | 69.44 | 69.52 | 70.21 | 0.12
2020-10-21 | 69.36 | 69.69 | 69.47 | 0.48
2020-10-22 | 68.29 | 67.84 | 68.23 | 0
2020-10-23 | 67.31 | 67.16 | 67.49 | -0.22
2020-10-26 | 67.20 | 67.38 | 67.18 | 0
2020-10-27 | 66.31 | 66.23 | 66.52 | -0.12
2020-10-28 | 66.58 | 67.09 | 66.53 | 0
2020-10-29 | 66.31 | 66.3 | 66.77 | -0.01
2020-10-30 | 67.08 | 66.14 | 67.62 | -1.37
Table 17: SH600028 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 3.94 | 3.94 | 3.94 | 0
2020-10-12 | 3.95 | 3.96 | 4.03 | 0.26
2020-10-13 | 3.93 | 3.94 | 3.89 | -0.26
2020-10-14 | 3.92 | 3.90 | 3.91 | 0.51
2020-10-15 | 3.92 | 3.91 | 3.92 | -0.26
2020-10-16 | 3.94 | 3.93 | 3.93 | 0.26
2020-10-19 | 3.92 | 3.92 | 3.92 | 0
2020-10-20 | 3.92 | 3.92 | 3.92 | 0
2020-10-21 | 3.92 | 3.93 | 3.99 | 0.26
2020-10-22 | 3.93 | 3.92 | 3.92 | 0.26
2020-10-23 | 3.90 | 3.90 | 3.93 | 0
2020-10-26 | 3.90 | 3.90 | 3.90 | 0
2020-10-27 | 3.88 | 3.88 | 3.88 | 0
2020-10-28 | 3.86 | 3.87 | 3.87 | 0.26
2020-10-29 | 3.88 | 3.88 | 3.87 | 0
2020-10-30 | 3.92 | 3.9 | 3.92 | 0
Table 18: SH600104 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 19.87 | 19.77 | 19.29 | 0.52
2020-10-12 | 19.96 | 20.07 | 20.00 | 0.58
2020-10-13 | 20.05 | 20.65 | 20.02 | -3.14
2020-10-14 | 20.34 | 20.53 | 20.33 | 0
2020-10-15 | 20.71 | 20.51 | 20.83 | -1.05
2020-10-16 | 20.39 | 20.59 | 20.43 | 1.05
2020-10-19 | 20.39 | 20.37 | 20.51 | -0.10
2020-10-20 | 20.80 | 21.11 | 20.81 | 0
2020-10-21 | 21.41 | 21.57 | 21.39 | -0.84
2020-10-22 | 21.17 | 21.19 | 21.66 | 0.10
2020-10-23 | 21.49 | 21.05 | 21.49 | 0
2020-10-26 | 21.26 | 21.18 | 21.36 | -0.42
2020-10-27 | 21.13 | 21.12 | 21.20 | -0.05
2020-10-28 | 21.59 | 21.67 | 21.91 | 0.42
2020-10-29 | 22.61 | 22.82 | 22.52 | -1.10
2020-10-30 | 23.65 | 23.06 | 23.79 | -3.08
Table 19: SH600276 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 91.31 | 91.97 | 91.33 | 0
2020-10-12 | 93.85 | 94.03 | 94.59 | 0.20
2020-10-13 | 93.66 | 93.75 | 93.46 | -0.10
2020-10-14 | 93.24 | 92.95 | 93.27 | 0
2020-10-15 | 93.23 | 93.34 | 92.97 | -0.12
2020-10-16 | 92.56 | 92.83 | 91.00 | -0.30
2020-10-19 | 91.83 | 91.27 | 91.99 | -0.62
2020-10-20 | 90.51 | 91.13 | 90.22 | -0.69
2020-10-21 | 89.64 | 89.50 | 89.64 | 0
2020-10-22 | 89.31 | 89.39 | 89.15 | -0.09
2020-10-23 | 89.20 | 88.29 | 89.27 | 0
2020-10-26 | 89.01 | 89.20 | 89.03 | 0
2020-10-27 | 88.96 | 89.23 | 89.21 | 0.30
2020-10-28 | 90.84 | 91.30 | 90.94 | 0.51
2020-10-29 | 91.05 | 91.50 | 90.77 | -0.50
2020-10-30 | 88.56 | 88.65 | 87.35 | -0.10
Table 20: SH600309 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 72.02 | 72.16 | 72.16 | 0.20
2020-10-12 | 76.01 | 77.03 | 78.66 | 1.47
2020-10-13 | 77.02 | 79.37 | 76.97 | 0
2020-10-14 | 80.48 | 80.24 | 80.63 | -0.35
2020-10-15 | 79.70 | 79.61 | 79.65 | 0
2020-10-16 | 78.75 | 79.73 | 79.40 | 1.41
2020-10-19 | 79.02 | 79.30 | 79.36 | 0.40
2020-10-20 | 79.11 | 80.01 | 79.31 | 1.30
2020-10-21 | 79.51 | 79.59 | 79.46 | 0
2020-10-22 | 78.42 | 78.91 | 78.54 | 0.71
2020-10-23 | 79.73 | 77.40 | 81.01 | -3.36
2020-10-26 | 76.34 | 76.32 | 77.01 | -0.03
2020-10-27 | 77.33 | 77.60 | 77.22 | -0.39
2020-10-28 | 79.78 | 82.30 | 79.97 | 3.64
2020-10-29 | 79.00 | 78.83 | 76.16 | 0.25
2020-10-30 | 80.70 | 78.25 | 80.65 | 0
Table 21: SH600346 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 19.05 | 19.01 | 18.90 | 0.22
2020-10-12 | 19.84 | 19.89 | 20.30 | 0.27
2020-10-13 | 20.47 | 20.88 | 21.10 | 2.21
2020-10-14 | 20.87 | 21.07 | 20.90 | 1.08
2020-10-15 | 20.79 | 20.57 | 20.74 | 1.19
2020-10-16 | 20.42 | 20.37 | 20.41 | 0
2020-10-19 | 20.52 | 20.38 | 19.55 | 0.75
2020-10-20 | 20.21 | 20.49 | 20.26 | 1.51
2020-10-21 | 20.30 | 20.09 | 20.30 | 0
2020-10-22 | 19.33 | 19.48 | 18.70 | -0.81
2020-10-23 | 19.66 | 19.50 | 19.56 | 0.86
2020-10-26 | 19.53 | 19.46 | 20.11 | -0.38
2020-10-27 | 19.04 | 19.20 | 18.29 | -0.86
2020-10-28 | 19.52 | 20.34 | 19.53 | 0
2020-10-29 | 20.01 | 19.99 | 20.00 | 0
2020-10-30 | 19.62 | 19.49 | 19.56 | 0.70
Table 22: SH600519 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 1703.75 | 1696.41 | 1703.15 | 0
2020-10-12 | 1742.00 | 1748.24 | 1736.77 | -0.37
2020-10-13 | 1741.31 | 1739.13 | 1741.28 | 0
2020-10-14 | 1730.13 | 1729.03 | 1729.02 | 0
2020-10-15 | 1732.50 | 1724.30 | 1732.14 | 0
2020-10-16 | 1722.99 | 1713.50 | 1730.24 | -0.57
2020-10-19 | 1696.00 | 1697.68 | 1693.43 | -0.10
2020-10-20 | 1729.90 | 1733.38 | 1730.82 | 0
2020-10-21 | 1727.52 | 1731.80 | 1727.46 | 0
2020-10-22 | 1731.85 | 1743.00 | 1729.72 | -0.67
2020-10-23 | 1739.00 | 1722.99 | 1742.37 | -0.96
2020-10-26 | 1645.84 | 1642.00 | 1649.04 | -0.23
2020-10-27 | 1618.55 | 1625.31 | 1618.72 | 0
2020-10-28 | 1657.57 | 1665.29 | 1659.36 | 0.46
2020-10-29 | 1705.00 | 1688.87 | 1757.35 | -0.97
2020-10-30 | 1688.98 | 1666.60 | 1688.38 | 0
Table 23: SH601012 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 82.43 | 81.24 | 82.18 | 1.59
2020-10-12 | 82.51 | 82.75 | 82.07 | -0.32
2020-10-13 | 81.85 | 82.54 | 81.72 | -0.92
2020-10-14 | 80.46 | 80.74 | 81.99 | 0.37
2020-10-15 | 78.27 | 77.64 | 76.49 | 0.84
2020-10-16 | 76.37 | 76.02 | 76.87 | -0.47
2020-10-19 | 72.00 | 71.09 | 69.06 | 1.21
2020-10-20 | 71.46 | 73.14 | 71.31 | -2.24
2020-10-21 | 72.21 | 72.47 | 71.59 | -0.35
2020-10-22 | 70.19 | 70.83 | 70.12 | -0.85
2020-10-23 | 69.69 | 67.71 | 70.21 | -2.64
2020-10-26 | 69.49 | 70.20 | 69.69 | 0.95
2020-10-27 | 69.46 | 71.38 | 69.44 | 0
2020-10-28 | 72.12 | 72.39 | 71.70 | -0.36
2020-10-29 | 73.88 | 74.33 | 74.08 | 0.6
2020-10-30 | 75.88 | 74.80 | 75.32 | 1.44
Table 24: SH601088 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 16.74 | 16.56 | 16.76 | -1.09
2020-10-12 | 16.80 | 16.73 | 16.80 | 0
2020-10-13 | 16.66 | 16.77 | 16.81 | 0.67
2020-10-14 | 16.59 | 16.66 | 16.58 | 0
2020-10-15 | 16.91 | 16.77 | 17.02 | -0.85
2020-10-16 | 16.98 | 16.97 | 17.04 | -0.06
2020-10-19 | 16.88 | 16.86 | 16.93 | -0.12
2020-10-20 | 16.71 | 16.82 | 16.96 | 0.67
2020-10-21 | 16.73 | 16.76 | 16.74 | 0
2020-10-22 | 16.70 | 16.87 | 16.72 | 1.03
2020-10-23 | 16.67 | 16.71 | 16.70 | 0.24
2020-10-26 | 16.65 | 16.57 | 16.64 | 0
2020-10-27 | 16.77 | 16.75 | 17.05 | -0.12
2020-10-28 | 16.57 | 16.64 | 16.59 | 0.43
2020-10-29 | 16.48 | 16.55 | 16.50 | 0.43
2020-10-30 | 16.81 | 16.63 | 16.84 | -1.09
Table 25: SH601225 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 8.96 | 8.89 | 9.33 | -0.83
2020-10-12 | 8.99 | 8.99 | 8.98 | 0
2020-10-13 | 9.11 | 9.25 | 9.09 | -1.67
2020-10-14 | 9.09 | 9.13 | 9.10 | 0
2020-10-15 | 9.40 | 9.14 | 9.45 | -3.10
2020-10-16 | 9.30 | 9.35 | 9.35 | 0.60
2020-10-19 | 9.28 | 9.17 | 9.25 | 1.31
2020-10-20 | 9.12 | 9.17 | 9.24 | 0.60
2020-10-21 | 9.03 | 9.08 | 9.04 | 0.60
2020-10-22 | 8.92 | 9.01 | 8.94 | 1.07
2020-10-23 | 8.99 | 8.90 | 8.98 | 1.07
2020-10-26 | 8.83 | 8.91 | 8.82 | -0.95
2020-10-27 | 8.83 | 8.88 | 8.85 | 0.60
2020-10-28 | 8.81 | 8.92 | 8.82 | 0
2020-10-29 | 8.74 | 8.79 | 8.74 | 0
2020-10-30 | 8.90 | 8.74 | 8.91 | 0
Table 26: SH601318 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 78.41 | 77.88 | 78.92 | -0.69
2020-10-12 | 80.38 | 80.96 | 80.01 | -0.76
2020-10-13 | 81.11 | 81.46 | 82.15 | 0.46
2020-10-14 | 81.29 | 81.35 | 81.29 | 0
2020-10-15 | 82.12 | 81.50 | 80.97 | 0.81
2020-10-16 | 82.16 | 82.13 | 82.28 | -0.04
2020-10-19 | 82.73 | 81.91 | 81.98 | 1.08
2020-10-20 | 81.19 | 81.26 | 81.31 | 0.09
2020-10-21 | 81.98 | 82.50 | 82.02 | 0
2020-10-22 | 81.35 | 82.36 | 81.25 | -1.32
2020-10-23 | 84.51 | 83.75 | 84.79 | -1.00
2020-10-26 | 82.88 | 82.10 | 83.24 | -1.02
2020-10-27 | 81.10 | 81.29 | 81.12 | 0
2020-10-28 | 79.29 | 79.65 | 78.97 | -0.47
2020-10-29 | 78.81 | 79.16 | 78.40 | -0.46
2020-10-30 | 78.31 | 77.76 | 78.40 | -0.72
Table 27: SH601398 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 4.91 | 4.92 | 4.93 | 0.20
2020-10-12 | 4.94 | 4.96 | 5.02 | 0.41
2020-10-13 | 4.93 | 4.92 | 4.99 | -0.20
2020-10-14 | 4.92 | 4.92 | 4.92 | 0
2020-10-15 | 4.95 | 4.93 | 4.94 | 0.41
2020-10-16 | 5.00 | 4.99 | 5.08 | -0.20
2020-10-19 | 5.03 | 5.02 | 5.03 | 0
2020-10-20 | 4.96 | 4.97 | 4.97 | 0.20
2020-10-21 | 5.02 | 5.05 | 5.02 | 0
2020-10-22 | 5.04 | 5.05 | 5.04 | 0
2020-10-23 | 5.07 | 5.07 | 5.08 | 0
2020-10-26 | 5.04 | 5.02 | 5.05 | -0.41
2020-10-27 | 5.02 | 5.03 | 5.02 | 0
2020-10-28 | 5.00 | 5.01 | 5.00 | 0
2020-10-29 | 4.98 | 4.97 | 4.93 | 0.20
2020-10-30 | 4.93 | 4.92 | 4.93 | 0
Table 28: SH601766 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 5.56 | 5.56 | 5.56 | 0
2020-10-12 | 5.64 | 5.65 | 5.63 | -0.18
2020-10-13 | 5.62 | 5.61 | 5.61 | 0.18
2020-10-14 | 5.58 | 5.58 | 5.67 | 0
2020-10-15 | 5.56 | 5.55 | 5.57 | -0.18
2020-10-16 | 5.57 | 5.59 | 5.58 | 0.36
2020-10-19 | 5.59 | 5.57 | 5.46 | 0.36
2020-10-20 | 5.54 | 5.55 | 5.53 | -0.18
2020-10-21 | 5.52 | 5.60 | 5.52 | 0
2020-10-22 | 5.55 | 5.54 | 5.53 | 0.18
2020-10-23 | 5.56 | 5.56 | 5.55 | 0
2020-10-26 | 5.56 | 5.54 | 5.56 | 0
2020-10-27 | 5.52 | 5.52 | 5.53 | 0
2020-10-28 | 5.49 | 5.51 | 5.49 | 0
2020-10-29 | 5.48 | 5.46 | 5.46 | 0.36
2020-10-30 | 5.46 | 5.40 | 5.47 | -1.09
Table 29: SH601857 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 4.13 | 4.14 | 4.13 | 0
2020-10-12 | 4.16 | 4.17 | 4.23 | 0.24
2020-10-13 | 4.15 | 4.14 | 4.14 | 0.24
2020-10-14 | 4.11 | 4.11 | 4.07 | 0
2020-10-15 | 4.11 | 4.10 | 4.11 | 0
2020-10-16 | 4.11 | 4.12 | 4.12 | 0.24
2020-10-19 | 4.11 | 4.11 | 4.18 | 0
2020-10-20 | 4.09 | 4.09 | 4.09 | 0
2020-10-21 | 4.09 | 4.11 | 4.09 | 0
2020-10-22 | 4.09 | 4.09 | 4.09 | 0
2020-10-23 | 4.12 | 4.13 | 4.14 | 0.24
2020-10-26 | 4.09 | 4.11 | 4.10 | 0.49
2020-10-27 | 4.10 | 4.09 | 4.09 | 0.24
2020-10-28 | 4.09 | 4.09 | 4.08 | 0
2020-10-29 | 4.06 | 4.07 | 4.06 | 0
2020-10-30 | 4.10 | 4.08 | 4.10 | 0
Table 30: SH601888 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 201.10 | 204.84 | 198.49 | -1.689
2020-10-12 | 208.30 | 211.60 | 215.31 | 1.48
2020-10-13 | 209.90 | 214.79 | 209.15 | -2.19
2020-10-14 | 208.11 | 209.77 | 208.28 | 0
2020-10-15 | 202.91 | 202.48 | 202.72 | 0
2020-10-16 | 202.72 | 203.40 | 202.91 | 0
2020-10-19 | 200.01 | 199.99 | 200.40 | -0.01
2020-10-20 | 200.59 | 201.16 | 198.40 | -0.26
2020-10-21 | 195.71 | 195.47 | 195.37 | 0.11
2020-10-22 | 191.53 | 195.93 | 192.37 | 1.97
2020-10-23 | 193.41 | 194.30 | 189.56 | -0.4
2020-10-26 | 185.81 | 184.07 | 186.45 | -0.78
2020-10-27 | 184.76 | 184.74 | 184.15 | 0.01
2020-10-28 | 197.18 | 199.79 | 197.33 | 0
2020-10-29 | 202.81 | 201.80 | 210.94 | -0.45
2020-10-30 | 203.34 | 200.05 | 203.04 | 1.48
Table 31: SH601899 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 6.24 | 6.21 | 6.23 | 0.49
2020-10-12 | 6.54 | 6.56 | 6.77 | 0.33
2020-10-13 | 6.50 | 6.59 | 6.49 | -1.46
2020-10-14 | 6.52 | 6.49 | 6.51 | 0.49
2020-10-15 | 6.57 | 6.56 | 6.58 | 0
2020-10-16 | 6.62 | 6.60 | 6.65 | -0.33
2020-10-19 | 6.61 | 6.67 | 6.64 | 0.98
2020-10-20 | 6.72 | 6.77 | 6.68 | -0.81
2020-10-21 | 6.91 | 6.95 | 6.98 | 0.65
2020-10-22 | 6.91 | 6.96 | 6.90 | 0
2020-10-23 | 6.96 | 6.87 | 6.97 | -1.46
2020-10-26 | 7.00 | 7.03 | 7.03 | 0.49
2020-10-27 | 7.01 | 7.06 | 7.00 | 0
2020-10-28 | 7.06 | 7.16 | 7.06 | 0
2020-10-29 | 7.05 | 7.02 | 7.17 | -0.49
2020-10-30 | 7.16 | 6.98 | 7.16 | 0
Table 32: SH601939 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 6.14 | 6.13 | 6.14 | 0
2020-10-12 | 6.18 | 6.23 | 6.17 | -0.81
2020-10-13 | 6.19 | 6.17 | 6.19 | 0
2020-10-14 | 6.17 | 6.16 | 6.16 | 0
2020-10-15 | 6.23 | 6.22 | 6.23 | 0
2020-10-16 | 6.40 | 6.40 | 6.38 | 0
2020-10-19 | 6.57 | 6.51 | 6.64 | -0.98
2020-10-20 | 6.34 | 6.35 | 6.10 | -0.16
2020-10-21 | 6.38 | 6.43 | 6.38 | 0
2020-10-22 | 6.42 | 6.46 | 6.42 | 0
2020-10-23 | 6.50 | 6.49 | 6.51 | -0.16
2020-10-26 | 6.44 | 6.45 | 6.43 | -0.16
2020-10-27 | 6.39 | 6.38 | 6.40 | -0.16
2020-10-28 | 6.30 | 6.28 | 6.31 | -0.33
2020-10-29 | 6.30 | 6.30 | 6.28 | 0
2020-10-30 | 6.33 | 6.27 | 6.28 | 0.98
Table 33: SH603288 daily return Date | Price at 11:20 | Real price at 14:50 | Predicted price at 14:50 | Daily return (%)
---|---|---|---|---
2020-10-9 | 164.56 | 162.62 | 163.56 | 1.20
2020-10-12 | 171.30 | 172.28 | 182.68 | 0.60
2020-10-13 | 173.20 | 172.03 | 176.88 | -0.72
2020-10-14 | 171.25 | 171.80 | 171.43 | 0.34
2020-10-15 | 169.36 | 170.87 | 168.93 | -0.93
2020-10-16 | 168.27 | 168.41 | 168.82 | 0.09
2020-10-19 | 164.46 | 163.13 | 163.12 | 0.82
2020-10-20 | 167.71 | 169.59 | 172.16 | 1.16
2020-10-21 | 168.60 | 168.99 | 169.20 | 0.24
2020-10-22 | 167.18 | 168.20 | 167.24 | 0
2020-10-23 | 166.50 | 164.96 | 166.40 | 0
2020-10-26 | 161.49 | 162.00 | 161.92 | 0.31
2020-10-27 | 163.84 | 164.23 | 163.63 | -0.24
2020-10-28 | 165.89 | 166.00 | 167.59 | 0.07
2020-10-29 | 167.90 | 168.02 | 167.59 | -0.07
2020-10-30 | 162.45 | 159.45 | 157.43 | 1.85