# Stress index strategy enhanced with financial news sentiment analysis for
the equity markets

Baptiste Lefort  CentraleSupélec, Paris-Saclay University  Ai For Alpha  Eric
Benhamou  Paris-Dauphine PSL  Ai For Alpha  Jean-Jacques Ohana  Ai For Alpha
David Saltiel  Ai For Alpha  Beatrice Guez  Ai For Alpha  Thomas Jacquot  Ai
For Alpha

This paper introduces a new risk-on risk-off strategy for the stock market,
which combines a financial stress indicator with a sentiment analysis done by
ChatGPT reading and interpreting Bloomberg daily market summaries. Forecasts
of market stress derived from volatility and credit spreads are enhanced when
combined with the financial news sentiment derived from GPT4. As a result, the
strategy shows improved performance, evidenced by higher Sharpe ratio and
reduced maximum drawdowns. The improved performance is consistent across the
NASDAQ, the S&P 500 and the six major equity markets, indicating that the
method generalises across equities markets.
Keywords: Market stress, Volatility, News sentiment, Investment strategy

JEL Codes: G14, D81, C55, G17

##  1 Introduction

Recent advancements in Natural Language Processing (NLP) with Large Language
Models (LLMs) have made the sentiment analysis of financial news by machines a
practical achievement and no longer just a dream. More precisely, Large
Language Models (LLMs) have marked a major step forward in processing large
contexts, exhibiting human-level performance on various professional and
academic benchmarks, although they still have limitations such as reliability
issues and limited context windows (OpenAI, 2023). Their ability to process
more context has shown particularly interesting applications in many business
areas (George and George, 2023). Hence exploring the potential to extract
either weak or strong signals from financial news to enhance a risk-on risk-
off investment strategy becomes highly pertinent.

Indeed, extracting sentiment from financial news is not new (Tetlock, 2007;
Schumaker and Chen, 2009), and finance has a longstanding tradition of
exploiting textual data (Kearney and Liu, 2014). More precisely, sentiment
analysis is a task where we aim at identifying the underlying sentiment in a
text. Many different types of models and methods can be employed for
identifying sentiment (Baccianella et al., 2010; Tang et al., 2023). Models
like BERT and its finance-focused version FinBERT have exposed their
application in the financial industry (Devlin et al., 2018; Araci, 2019).
These models have significantly increased the sentiment analysis’s precision
(Kant et al., 2018), giving a new opening to using the news for financial
decision-making. Likewise, SentiWordNet 3.0, provides an enhanced lexical
resource for sentiment analysis, showing about 20% accuracy improvement over
its earlier version (Baccianella et al., 2010). Recent advancements like
FinEntity focus on entity-level sentiment classification in financial texts,
demonstrating its utility in investment and regulatory applications (Tang et
al., 2023). Deep Learning application have also shown clear improvement and
proved their ability to provide consistently reliable sentiment to a complex
text (Zhang et al., 2018).

However, when scrutinized across various equity markets and extended out-of-
sample backtesting, these efforts proved to be less than compelling Xing et
al. (2018). Interpreting financial news has long been a complex task (Loughran
and McDonald, 2011), as it involves intricate concepts open to various
interpretations and contains time-sensitive information with an expiration
date of relevance. Moreover, the sentiment conveyed in financial news is often
influenced by human perception, and there are numerous underlying implications
(Ikizlerli et al., 2019; Elliott et al., 2018).

We hypothesize two things that can help solve the seminal problem of
interpreting news in order to forecast equity market regimes. First, the
emergence of Large Language Models (LLMs) could bring fresh perspectives to
this longstanding issue, potentially enhancing the interpretation of
ambiguities in financial news. Second, news sentiments should be combined with
other signals in order to have a robust risk-on risk-off strategy accross
major equity markets.

Hence, we present here a new approach to tackle risk-on, risk-off strategy for
the stock market. This approach integrates a financial stress indicator with
sentiment analysis performed by ChatGPT, which reads and interprets daily
market summaries from Bloomberg. Additionally, we present a strategy selection
method, alternating between the hybrid strategy that combines news signals and
stress index and another based only on the conventional stress index
indicator.

The rest of the paper is organized as follows. Section 2 provides a review of
existing literature on the application of ChatGPT in formulating financial
strategies. In section 3, we explain how our methodology differs from existing
studies and outline our principal contributions. Section 4 presents the
different types of data used: namely news, the stress index and investment
universe overwhich this new strategy is tested. Section 5 details the
comprehensive set of experiments conducted, highlighting the execution of 18
distinct tests. These tests are designed to ascertain the most effective
strategy from a range of alternatives, namely whether the news signal and the
stress index signal are effective or not. In total, six varied strategies are
evaluated across three disparate financial markets to assert whether the
results remains similar across various equity markets. In Section 6, we
provide an in-depth discussion of the result, with a particular focus on the
dynamically strategy that alternates between the stress index and the news
sentiment and the stress index alone. The findings reveal that this combined
method consistently outperforms other strategies in all three equity markets
evaluated. The superiority of this approach is highlighted by its higher
Sharpe and Calmar ratio (the ratio of return to maximum drawdown), when
compared with the other strategies that rely on the stress index, news alone,
the volatility index (VIX) based strategy, and a static combination of stress
with news. Section 7 concludes and suggests future works.

##  2 Related Works

In the field of finance and economics, numerous recent academic studies have
used ChatGPT, including (Hansen and Kazinnik, 2023), (Cowen and Tabarrok,
2023), (Korinek, 2023; Lopez-Lira and Tang, 2023), and (Noy and Zhang, 2023).
The capabilities of ChatGPT in various economic and professional contexts are
explored in several studies. (Cowen and Tabarrok, 2023) and (Korinek, 2023)
discuss the model’s role in economics education and research. (Hansen and
Kazinnik, 2023) focuses on how ChatGPT interprets Fedspeak, the complex
language used by the Federal Reserve. In a similar vein, (Lopez-Lira and Tang,
2023) delves into how to effectively prompt the model for stock return
forecasts. Additionally, (Noy and Zhang, 2023) highlights the model’s
enhancement of productivity in professional writing, while (Yang and Menczer,
2023) examines its ability to identify credible news sources. Beside the
increasing number of use of LLMs for sentiment analysis, using news sentiment
in investment strategy is not new. Many studies involve using more classical
methods like lexical approaches for developing news sentiment indicator
(Januário et al., 2022).

The development and utilization of sentiment indicators for trading strategies
have also seen significant progress. The study A Deep Learning Approach with
Extensive Sentiment Analysis for Quantitative Investment by (Li et al., 2023)
introduces an approach that incorporates news content for sentiment analysis
in quantitative investment, achieving interesting annualized returns. In the
realm of cryptocurrency, (Yeoh et al., 2023) analyze the impact of news
sentiment on the prices of cryptocurrencies using the FinBERT model for
financial sentiment analysis. Their findings indicate an influence of
sentiment on price movements. (Nakagawa et al., 2022) also contribute to this
field with their investment strategy that exploits the lead-lag effect in
company relationships using sentiment analysis. Furthermore, (Yang, 2023)
demonstrates the profitability of investment strategies based on analyst
opinions extracted from text analysis, suggesting an improved daily returns.
The market stress is also a widely studied index for combining it with news
sentiment indicator and shows promising results (Smales, 2015, 2016; Lin et
al., 2023). These studies collectively highlight the growing importance and
effectiveness of sentiment analysis as a tool for developing investment
strategies.

##  3 Key contributions

In this paper, we present a strategy that combines a stress index with news
sentiment analysis in a way that has never been addressed. We built a crisis-
resistant strategy that yields a constant performance over a long period. Our
approach involves several key steps. Firstly, we generate a signal by
analyzing news sentiment, utilizing Bloomberg’s extensive daily market
summaries. This signal provides a broad, market-wide perspective, focusing on
impactful financial news. Our findings reveal that using this signal alone is
not effective. However, integrating it with the stress index significantly
enhances our strategy, making it more consistent and robust. This approach not
only improves performance but also demonstrates the broader applicability of
the stress index strategy on a macroeconomic level.

The principal contribution of the paper are the following:

  1. 1.

News alone is not enough: Although we use premium and heavily scrutinized
data, that is the Bloomberg’s daily market reports to create a news sentiment-
based macro indicator thanks to a multiple prompt approach using ChatGPT that
has demonstrated a statistical correlation with equity market returns, we
experienced poor results when using the news sentiment indicator alone.
However, when combined with a stress index combining other data type like
volatility and credit spread, we get improved performance over our benchmark
of a naive long only strategy as well as other strategies based purely on
stress index or news.

  2. 2.

It is crucial to have an alternative method when the news-based signal becomes
inefficient: We present a method to switch between the stress index and the
combination of the stress index and the news sentiments based on the
persistence of the overall performances of one strategy over the other one.
This helps us to mitigate periods of time where the news sentiment indicator
becomes inefficient.

  3. 3.

The method works on various equity markets: Through empirical validation, we
confirm that the integration of the stress index and news signal strategy,
coupled with the transition to a pure risk index in the event of prolonged
underperformance, consistently improves outcomes in various equities markets.
This observation underscores the strategy’s ability to demonstrate persistent
promising results, indicating its capacity to generalize across major equity
markets.

##  4 Data

To create an investment strategy on various equity markets leveraging
financial news and a stress index indicator, we need to collect multiple data:
reliable financial news, stress index and investment universe. Let us
introduce each of these one at a time.

###  4.1 News Data

We use professional news from Bloomberg. Bloomberg provides daily market
wraps, that are daily summaries of the most important news. They are available
since 2010 with growing frequency after 2011. These texts contain both
numerical and literal values and are gathering the most informative content
and news of the day. Daily market wraps are produced multiple times during the
day and specialized for the three regions: the US, Europe and Asia. In order
to have a consistent data set, we rely on the latest available news before the
European market opening, that is the end of day Asian market wraps. Hence, we
collect 3627 daily market wraps. Each of these wraps is about 7000 characters,
representing with line breaks about 140 lines or about 5 pages long. Here is
below an example of Bloomberg Daily Market Wraps for the day. The range of the
contents are any news that may impact financial markets including equity,
currencies, cryptocurrencies, bonds and commodities.

\- Investor sentiment remains quite negative in China despite a rally in
global stocks during the past two months of 2023, Nomura Group analysts
including Chetan Sethin Singapore wrote in a client note. In China, there have
been more signs of support for the economy, but equity investors still do not
appear convinced.
\- Bond Traders Seize on 4% Yields, Confident Fed Rate Cuts Coming
\- The Australian dollar fell 0.2% to $0.6701 …

####  4.1.1 News signal

We follow the same methodology as in (Lefort et al., 2024), and use a two-step
approach to break the sentiment analysis into simpler subtasks for which
chatGPT is better suited, namely text summary and keywords identification.
Here are the steps we follow:

  1. 1.

First, we collect the daily market summaries from Bloomberg to stay updated on
financial trends.

  2. 2.

Next, we ask ChatGPT to generate 15 notable headlines every day, ensuring we
capture the most significant events.

  3. 3.

Once we have our headlines, we take a moment to assess their tone, deciding
whether they convey positive, negative, or indecisive sentiment. Hence, we
calculate the daily count of news evaluated in each category, resulting in a
numerical score for each day. It is a simple scale: -1 points for a negative
sentiment, 0 for indecisive, and +1 for the the days that are positively
charged.

  4. 4.

With these scores in hand, we don’t just look at one day in isolation; we add
them up across a 10-day period to get a broader view of the market’s mood. By
averaging these daily scores over 10 days, we’re able to smooth out the ups
and downs and pinpoint the general trend in news sentiment.

  5. 5.

We then apply a statistical method called z-scoring to this 10-day sentiment
average, which helps us understand how strongly the news is leaning compared
to the norm.

  6. 6.

The final transformation involves calculating the mean of the z-scored signal
over the preceding 10 days.

  7. 7.

Finally, we obtain a simple binary news indicator. If the z-score shows a
positive trend, we set it up as 1, indicating a thumbs-up for market
sentiment. If the trend dips negative, we set it to 0, signaling a more
cautious outlook. This binary indicator serves as a quick reference to gauge
the overall sentiment in the financial news, allowing us to make more informed
decisions based on the prevailing mood in the market.

###  4.2 Stress Index

We rely on the stress index as presented in (Guilleminot et al., 2014) to
capture contagion effects in the market and anticipates future crisis. The
indicator is documented to be predictive since it aims at detecting period of
high volatility. We prefer this financial stress indicator to the VIX as it
has numerous advantages in detecting market contagion:

  * •

Comprehensive Risk Analysis: The stress index combines a broad range of market
prices of risk, including CDS contracts, providing a more detailed view of
market stress compared to the VIX, which focuses mainly on S&P 500 index
options.

  * •

Enhanced Normalization Techniques: It employs z-score based normalization,
facilitating more effective comparability across various markets, thereby
allowing for a nuanced understanding of stress levels in different market
segments.

  * •

Detection of Market Contagion: It is specially designed to capture
interconnectedness and contagion in financial markets, reflecting the complex
dynamics often missed by other indicators like the VIX.

  * •

Detection of Crises: Incorporating CDS contracts from the main banks,
insurances and governments enables it to capture signals related to crises.

####  4.2.1 Stress Signal computation

The step to compute the stress index are the following:

  1. 1.

We start by gathering market data for a variety of assets, which includes
indicators like the VIX index, TED spread, and CDS index, along with the
volatility data for major equity, bond, and commodity markets.

  2. 2.

Next, we calculate the price of risk for each asset by standardizing the data
with z-scoring, which adjusts for the variability and scales the risk price in
terms of its deviation from the mean.

  3. 3.

Then, we aggregate these z-scores by their respective categories such as
equities, emerging bonds, government bonds, financial stocks, foreign
exchange, commodities, interest rates, and corporate credit to form category-
specific stress indicators.

  4. 4.

We take the average of these category-specific stress indicators to compile a
comprehensive stress index that reflects the overall market conditions.

  5. 5.

Finally, we scale the resulting average to fall between 0 and 1 by applying
the cumulative distribution function (norm.cdf) to the computed stress index,
which normalizes our final index value.

Because the stress index final result is a number between 0 and 1 thanks to
the cumulative distribution function of the normal distribution, we directly
get a stress index signal.

###  4.3 Markets Tested

We test the strategy on six different equity markets: NASDAQ, S&P500, Nikkei,
Euro Stoxx, and Emerging Markets from January 2005 to 2024.

We compare the strategy on three cases:

  1. 1.

The S&P500 alone.

  2. 2.

The NASDAQ alone.

  3. 3.

An equally weighted basket of the aforementioned six equity markets.

##  5 Experiments

###  5.1 Transaction Costs

To have very realistic simulations, we incorporate linear transaction costs,
denoted by $b$ equals to 2 basis points for all our strategy ($b=0.0002$). To
put some mathematical notations, assuming we have $n$ assets whose weights are
denoted by $(w_{t}^{i})_{i=1,...,n}$ and that the daily returns of our assets
are denoted by $(r_{t}^{i})_{i=1,...,n}$, we compute our strategy value
recursively with the convention that the strategy starts at 1 at time 0:
$S_{0}=1$ as follows:

$S_{t}=S_{t-1}\times(1+\sum_{i=1}^{n}w_{t-1}^{i}\times
r_{t}^{i}-b|w_{t}-w_{t-1}|)$ (1)

###  5.2 The six different strategies

In order to evaluate whether the news signal is predictive or not, we compare
six strategies and run in total eighteen experiments as we compare these
strategies over 3 different markets, namely the US market by testing these
strategies on the NASDAQ, the S&P 500, and an equally weighted strategy
combining the 6 major equity markets presented in section 4.3.

Here is the list of the 6 different strategies:

  1. 1.

Long only: also referred to as the Benchmark in our comparison figures, this
strategy takes a long only position on the tested markets. We term this the
Benchmark because our aim is to assess whether the other quantitative
strategies exhibit superior performance compared to this one.

  2. 2.

VIX: Weights are directly related to the VIX signal where we assume that
periods of stress are identified by a VIX level above its 80 percentile which
is around 26 percents.

  3. 3.

SI for Stress Index: Weights are directly related to the stress index signal.

  4. 4.

News: Weights are directly related to the news signal

  5. 5.

SI News: Weights are obtained by the straighforward multiplication of the two
signals, namely the stress index times the news signal.

  6. 6.

Dynamic SI News: this strategy is a dynamic selection between the strategy
based on stress index with news and the strategy using the stress index alone.
More details can be found in the section 5.3

In total we have 18 experiments as we compare these 6 strategies over the
three cases listed below:

  1. 1.

Test on the NASDAQ.

  2. 2.

Test on the S&P 500.

  3. 3.

Test on the 6 major world equity markets.

Also to compare these strategies with a naive long only strategy, we also
compute a long only strategy that shares the same volatility as the better
performing strategy. Hence, we calculate ex-post the volatility of our best
strategies and rescale the benchmark to have the same volatility to compare
track records with the benchmark in a volatility consistent way.

###  5.3 Dynamic Strategy Selection

The primary goal involves dynamically shifting between two investment
strategies: one only reliant on the stress index (SI) and another that
incorporates news signals alongside the stress index (SI+News). This strategic
alternation aims to navigate efficiently through periods where the inclusion
of news signals doesn’t substantially improve the performance of the strategy
compared to using the stress index alone. Empirical observations reveal a
consistent pattern: the SI+News strategy either significantly outperforms or
underperforms the SI-only strategy. Consequently, a strategic selector
mechanism has been developed. This mechanism computes the Sharpe ratio
(defined as the ratio of the excess return over the annualized volatility as
in (Sharpe, 1966, 1975)) over a span of 250 observations, equivalent to an
annual rolling period, for the preceding month and consequently selects the
strategy that demonstrated superior performance for the forthcoming month.
Notably, there are intervals where the SI-only strategy surpasses the combined
SI+News strategy. The news signals predominantly act as a performance enhancer
during specific scenarios, particularly in times of crisis.

To elucidate this strategic selection, refer to Table 1. For each month, the
Sharpe ratios of the two strategies are calculated. The strategy for the
subsequent month is chosen based on the higher Sharpe ratio. For example, in
December 2022, the SI+News strategy, with a Sharpe ratio of 0.9, surpasses the
SI-only strategy, which has a Sharpe ratio of 0.4. Consequently, for December,
January, and February, the dominant SI+News strategy is selected. Conversely,
in March and April, the SI-only strategy becomes dominant, leading to its
selection in April and May.

Table 1: Illustration of the Method for Switching Between the SI and SI+News Strategies Month | Sharpe SI | Sharpe SI+News | Selected Strategy
---|---|---|---
Dec 2022 | 0.4 | 0.9 | …
Jan 2023 | -0.1 | 0.7 | SI+News
Feb 2023 | 0.2 | 0.5 | SI+News
Mar 2023 | 0.5 | 0.1 | SI+News
Apr 2023 | 1.2 | 0.6 | SI
May 2023 | … | … | SI
… | … | … | …

To validate the predictive efficacy of each strategy, it is necessary to
demonstrate that the strategy selection exhibits a degree of persistence. The
primary indicator of predictive capability is the frequency of selection for
each strategy. Over the period from 2011 to 2024, the SI-only strategy was
selected 71% of the time, while the SI+News strategy was chosen 29% of the
time. A random selection mechanism would yield a 50% selection rate for each
strategy. Additionally, a graphical representation of the strategy selection
demonstrates that the SI+News strategy is chosen primarily during specific
periods, suggesting that the news signal is particularly beneficial during
certain times.

##  6 The results

We test our six different strategies from January 2005 to January 2024, hence
testing them over 19 years. Our comparative analysis, as detailed in tables 2,
3, and 4, highlights some consistency in the comparison of the different
investment strategies for the S&P 500, NASDAQ, and major equity markets with
the uniform outperformance of the Dynamic SI+News strategy in terms of Sharpe
and Calmar ratios across all markets. It underscores its superior risk-
adjusted returns and drawdown management. In the three tables, in order to
have short column name, we denote Sharpe for the Sharpe ratio, Calmar for the
Calmar ratio, Vol for the annualized volatility and Max DD for the maximum
drawdowns. We recall that the Calmar ratio is defined as the annualized return
over the maximum drawdowns (see (Young, 1991)).

Table 2: Comparative Analysis of Investment Strategies for the S&P 500 Strategy | Sharpe | Calmar | Vol | Max DD | Turnover
---|---|---|---|---|---
Dynamic SI+News | 0.81 | 0.56 | 7.5% | 11% | 8.6
SI | 0.70 | 0.51 | 8.0% | 11% | 7.7
SI+News | 0.53 | 0.30 | 6.2% | 11% | 13.4
Long Only | 0.45 | 0.13 | 7.5% | 27% | n.a.
VIX | 0.42 | 0.17 | 8.9% | 22% | 18.5
News | 0.42 | 0.15 | 10.6% | 29% | 17.9

In more details, in the S&P 500 (Table 2), the Dynamic SI+News strategy
achieves not only the highest Sharpe ratio (0.81) but also the highest Calmar
ratio (0.56). This improvement of Sharpe ratio is quite important as the
benchmark strategy (the long only strategy) only achieves a Sharpe ratio of
0.45, making the Sharpe ratio improvement quite substantial. Similarly, in the
NASDAQ market (Table 3), the same strategy shows a remarkable Sharpe ratio of
0.89, further reinforcing its efficacy in a tech-heavy index. The analysis for
the major equity markets (Table 4) also corroborates the superiority of the
Dynamic SI+News strategy, particularly in managing market downturns, as
evidenced by its lower maximum drawdown (Max DD) compared to other strategies
like News and VIX.

If we refine our analysis on the Sharpe ratio, we can also notice that the
strategy based on the Stress index alone always comes second indicating that
the signals emitted by the stress index seem quite robust and more effective
than the ones using the VIX index. Regarding turnover, which measures the
frequency of trading within the portfolio, we observe notable differences
across strategies. For instance, in the S&P 500 (Table 2), the ’SI+News’
strategy exhibits the highest turnover rate at 13.4, indicating a more active
trading approach. This contrasts with strategies like ’SI’ and ’Dynamic SI
News’ which have lower turnover rates, suggesting a more passive strategy. The
buy and hold strategy has by definition no turnover as we hold the position
for ever. It is interesting to note that the Stress Index based strategy is
effectively more moderate in terms of turning the position compared to the VIX
based strategy.

Table 3: Comparative Analysis of Investment Strategies for the NASDAQ Strategy | Sharpe | Calmar | Vol | Max DD | Turnover
---|---|---|---|---|---
Dynamic SI+News | 0.89 | 0.62 | 9.2% | 13% | 8.6
SI | 0.84 | 0.53 | 9.8% | 16% | 7.6
Long Only | 0.62 | 0.20 | 9.2% | 28% | n.a.
SI+News | 0.61 | 0.38 | 7.5% | 12% | 13.4
VIX | 0.58 | 0.25 | 11.3% | 27% | 18.5
News | 0.43 | 0.15 | 12.3% | 34% | 17.9
Table 4: Comparative Analysis of Investment Strategies for the major equity markets Strategy | Sharpe | Calmar | Vol | Max DD | Turnover
---|---|---|---|---|---
Dynamic SI+News | 0.85 | 0.44 | 6.8% | 13% | 8.5
SI | 0.71 | 0.39 | 7.2% | 13% | 7.7
SI+News | 0.61 | 0.20 | 5.5% | 17% | 13.4
Long Only | 0.52 | 0.13 | 6.8% | 28% | n.a.
VIX | 0.42 | 0.15 | 8.4% | 23% | 18.4
News | 0.32 | 0.09 | 8.1% | 29% | 16.2

Similarly, in the NASDAQ (Table 3) and major equity markets (Table 4), the
’VIX’ and ’News’ strategies are characterized by higher turnover rates (18.5
and 17.9 for NASDAQ, 18.4 and 16.2 for major equity markets, respectively),
again reflecting a more active management style. This frequent trading could
be indicative of an attempt to capitalize on short-term market movements.

In terms of the Calmar ratio, which assesses the risk-adjusted performance of
an investment by comparing the average annual compounded rate of return and
the maximum drawdown, the ’Dynamic SI+News’ strategy consistently outperforms
others across all markets. This is evident from its higher Calmar ratios (0.56
for S&P 500, 0.62 for NASDAQ, and 0.44 for major equity markets). The superior
Calmar ratios suggest that this strategy not only provides higher returns but
also does so with less risk, as indicated by lower maximum drawdowns.

In contrast, strategies like ’Long Only’ and ’News’ show lower Calmar ratios,
implying that they bear a higher risk (as seen in their higher maximum
drawdowns). This could be a critical factor for investors who are risk-averse
and prefer strategies that limit potential losses.

Figures 1, 2, and 3 provide a visual representation of these findings and
compare the best performing strategy namely the ’Dynamic SI+News’ strategy
versus the passive long only benchmark. The subplot in all figures 1, 2, and 3
reveal how the Dynamic SI+News strategy dynamically adjusts its allocation,
contributing to its robust performance.

Figure 1: Comparison of the dynamic strategy combining Stress index and News
and Stress index alone using persistence of overperformance of one strategy
over the other one versus the naive long only strategy (rescaled at the same
volatility) for the S&P 500 universe. The first plot compares the two
strategies over time while the subplot provides the corresponding overall
allocation. Figure 2: Comparison of the dynamic strategy combining Stress
index and News and Stress index alone using persistence of overperformance of
one strategy over the other one versus the naive long only strategy (rescaled
at the same volatility) for the NASDAQ 100 universe. The first plot compares
the two strategies over time while the subplot provides the corresponding
overall allocation. Figure 3: Comparison of the dynamic strategy combining
Stress index and News and Stress index alone using persistence of
overperformance of one strategy over the other one versus the naive long only
strategy (rescaled at the same volatility) for the Major Equities Markets. The
first plot compares the two strategies over time while the subplot provides
the corresponding overall allocation.

All in all, when compiling our 18 experiments, our analysis demonstrates the
effectiveness of the Dynamic SI News strategy across different markets, with
its ability to adapt to market conditions and maintain superior performance
metrics standing out as a key takeaway.

Additionally we provide in the appendix figures 4 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15 the comparative performance of other investment strategies over
time, alongside their corresponding allocations and over the 3 different
market universes, namely the S&P 500, the NASDAQ, and Major Equities Markets.
For each figure, we give as the top plot the temporal performance comparison
with the benchmark, the long only strategy, while the subplot details the
overall allocation.

Figure 4 illustrates the performance of a news-based strategy against a naive
long-only strategy within the S&P 500 universe. This figure highlights the
temporal evolution of the strategy’s effectiveness in comparison to the
benchmark. In Figure 5, we observe a similar analysis within the NASDAQ
universe. The subplot is particularly insightful for understanding the
allocation differences underlined by the news-based strategy. Figure 6 extends
this comparison to the Major Equities Markets, offering a broader view of the
strategy’s applicability in a global context.

Figures 7, 8, and 9 delve into strategies that amalgamate news with stress
index data. These figures offer an intriguing perspective on the synergistic
effects of combining these two data sources across different market universes.

Figures 10, 11, and 12 focus on strategies purely based on the stress index.
These plots are crucial for understanding the standalone impact of the stress
index on the investment strategy.

Finally, Figures 13, 14, and 15 compare the VIX-based strategy with the naive
long-only approach. The temporal comparison and allocation subplots underscore
the unique characteristics and performance of the VIX-based strategy in
different market settings.

###  6.1 Intuition of the results

The integration of news signals with the stress index in investment
strategies, as demonstrated in the S&P 500, NASDAQ, and major equity markets
(Tables 2, 3, and 4), makes a lot of sense and is grounded in both empirical
results and theoretical underpinnings from financial literature.

####  6.1.1 Added Values of News

Intuitively incorporating news read by a machine into an investment strategy
based on the stress index can improve the strategy’s effectiveness for several
reasons:

  * •

Signal Reactivity: News can detect new trend or sentiment in the markets
quicker. This is effectively illustrated by the higher turnover of the pure
news strategy.

  * •

Objectivity and Consistency: Unlike human analysts, reading news by a machine
is less prone to cognitive biases or emotional responses. The news signal
should be quite consistent and objective.

  * •

Quality of the data: Since we use Bloomberg market wraps that are supposed to
capture all major news, the news signal should be triggered by any weak
signals from news.

  * •

Hidden pattern catching: The news signal is capable of capturing more complex
relationships between news items than a human can.

####  6.1.2 Empirical Evidence

As already presented, the Dynamic SI+News strategy, which combines both news
signals and stress index data, shows a remarkable performance across various
markets. In the S&P 500, this strategy not only achieves the highest Sharpe
ratio (0.81) but also a significant Calmar ratio (0.56), surpassing the
benchmark long only strategy’s Sharpe ratio of 0.45.

####  6.1.3 Theoretical Insights

Combining news signals with the stress index aligns with the principles of
Behavioral Finance, particularly as detailed in (Barber and Odean, 2008).
Their research demonstrates how news and media attention significantly
influence investor behavior, often leading to irrational decision-making based
on recent news rather than long-term trends. This indicates a potential edge
in combining news analysis with stress indices, which act as barometers for
market sentiment and stress. By integrating recent news, which potentially
triggers overreactions or underreactions, with the more steady and sentiment-
focused stress indices, a more balanced and informed investment the strategy
can be improved by capitalizing on the quick, news-driven shifts in investor
behavior while being anchored by the broader, more stable market insights
provided by the stress index.

####  6.1.4 Role of Stress Index

The stress index alone consistently ranks second in terms of Sharpe ratio
across different markets, underscoring its robustness. It acts as a gauge of
market tension and uncertainty, often signaling impending market corrections
or volatility, making it a crucial component of a holistic investment
strategy.

####  6.1.5 Turnover Analysis

Analyzing turnover rates sheds light on strategy aggressiveness. The ’SI+News’
strategy exhibits the highest turnover (13.4 in the S&P 500), indicative of an
active trading approach. This is quite logical as the news based signals is
supposed to react very quickly. The same applies to the VIX strategy that
seems to overreact to any high volatility environement. In contrast, ’SI’ and
’Dynamic SI+News’ strategies demonstrate more moderation, suggesting a
balanced approach that leverages the predictive power of stress indices while
avoiding excessive trading.

####  6.1.6 Impact on Maximum Drawdown and Calmar Ratio

The integration of news signals into the stress index strategy has a
significant impact on the maximum drawdown (Max DD) and, consequently, on the
Calmar ratio. This is evident in the comparative analysis of investment
strategies across various markets (Tables 2, 3, and 4).

##### Max DD Reduction

The Dynamic SI+News strategy consistently demonstrates a lower Max DD compared
to other strategies, especially the News-only strategy. For example, in the
S&P 500, the Max DD for Dynamic SI+News is 11%, markedly lower than the 29%
Max DD for the News strategy. This reduction in Max DD is crucial for risk-
averse investors and indicates enhanced strategy stability during market
downturns.

##### Enhanced Calmar Ratio

The Calmar ratio, which measures the return per unit of downside risk, is
notably higher in the Dynamic SI+News strategy across all markets. For
instance, in the S&P 500, this strategy achieves a Calmar ratio of 0.56,
surpassing the Stress Index (SI) alone at 0.51 and significantly outperforming
the News strategy at 0.15. This trend is consistent across the NASDAQ and
major equity markets.

##### Rationale for Improvement

Incorporating news into the stress index strategy enhances its ability to
adapt to market sentiments and trends rapidly. News provides real-time
insights and immediate market reactions, which, when combined with the stress
index’s broader market sentiment gauge, allows for a more dynamic and
responsive strategy. This combination effectively mitigates risks during
volatile periods, leading to a reduced Max DD and an improved Calmar ratio.

##### Literature Support

This finding aligns with the existing financial literature that emphasizes the
importance of combining various types of market information to achieve a more
comprehensive and robust investment strategy. For instance, (Tetlock, 2007)
highlights how media and news content significantly influence investor
sentiment and stock market dynamics, underscoring the value of incorporating
real-time news into investment strategies (Tetlock, 2007). Similarly, Baker et
al. (2016) in their study provide insights into how economic policy
uncertainty, derived from news media coverage, impacts market conditions and
investor behavior, further justifying the inclusion of news data alongside
stress index information in strategic decision-making(Tetlock, 2007).
Additionally, Da et al. (2011) through their work emphasize the influence of
media attention on stock prices and trading volumes, suggesting that the
attention garnered by specific news can be pivotal in financial market
movements and should be integrated into comprehensive investment strategies
(Da et al., 2011). These studies collectively support the synergy between
real-time news and stress index data, enhancing the ability to capture and
react to market anomalies and stress conditions, thereby improving the overall
risk-adjusted performance of the strategy.

##  7 Conclusion

This paper introduces a novel risk-on, risk-off strategy for the stock market,
leveraging a financial stress indicator combined with sentiment analysis
performed by ChatGPT on Bloomberg’s daily market summaries. The strategy
enhances market stress forecasts, which are based on volatility and credit
spreads, using financial news sentiment derived from GPT4. This results in a
significant improvement in performance, evidenced by a higher Sharpe ratio and
reduced maximum drawdowns. The method’s effectiveness is not limited to a
single market; it is consistent across various equity markets, including the
NASDAQ and S&P 500, as well as six other major equity markets, indicating its
broad applicability. There are many potential directions for future research.
Future works could investigate if the strategy can be applied to different
financial markets such as commodities, bonds, and foreign exchange markets, to
test its versatility and robustness. Additionally, it would be worthwhile to
investigate whether additional data sources, such as social media sentiment or
macroeconomic indicators, could further improve the strategy’s performance.
Identifying patterns in economic events where the news signal is most
effective is also a potential avenue for future research.

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## Figures

Figure 4: Comparison of the strategy based only on news versus the naive long
only strategy (rescaled at the same volatility) for the S&P 500 universe. The
first plot compares the two strategies over time while the subplot provides
the corresponding overall allocation. Figure 5: Comparison of the strategy
based only on news versus the naive long only strategy (rescaled at the same
volatility) for the NASDAQ 100 universe. The first plot compares the two
strategies over time while the subplot provides the corresponding overall
allocation. Figure 6: Comparison of the strategy based only on news versus the
naive long only strategy (rescaled at the same volatility) for the Major
Equities Markets. The first plot compares the two strategies over time while
the subplot provides the corresponding overall allocation. Figure 7:
Comparison of the strategy combining Stress index and News versus the naive
long only strategy (rescaled at the same volatility) for the S&P 500 universe.
The first plot compares the two strategies over time while the subplot
provides the corresponding overall allocation. Figure 8: Comparison of the
strategy combining Stress index and News versus the naive long only strategy
(rescaled at the same volatility) for the NASDAQ 100 universe. The first plot
compares the two strategies over time while the subplot provides the
corresponding overall allocation. Figure 9: Comparison of the strategy
combining Stress index and News versus the naive long only strategy (rescaled
at the same volatility) for the Major Equities Markets. The first plot
compares the two strategies over time while the subplot provides the
corresponding overall allocation. Figure 10: Comparison of the Stress Index
based strategy versus the naive long only strategy (rescaled at the same
volatility) for the S&P 500 universe. The first plot compares the two
strategies over time while the subplot provides the corresponding overall
allocation. Figure 11: Comparison of the Stress Index based strategy versus
the naive long only strategy (rescaled at the same volatility) for the NASDAQ
100 universe. The first plot compares the two strategies over time while the
subplot provides the corresponding overall allocation. Figure 12: Comparison
of the Stress Index based strategy versus the naive long only strategy
(rescaled at the same volatility) for the Major Equities Markets. The first
plot compares the two strategies over time while the subplot provides the
corresponding overall allocation. Figure 13: Comparison of the VIX based
strategy versus the naive long only strategy (rescaled at the same volatility)
for the S&P 500 universe. The first plot compares the two strategies over time
while the subplot provides the corresponding overall allocation. Figure 14:
Comparison of the VIX based strategy versus the naive long only strategy
(rescaled at the same volatility) for the NASDAQ 100 universe. The first plot
compares the two strategies over time while the subplot provides the
corresponding overall allocation. Figure 15: Comparison of the VIX based
strategy versus the naive long only strategy (rescaled at the same volatility)
for the Major Equities Markets. The first plot compares the two strategies
over time while the subplot provides the corresponding overall allocation.


$\displaystyle=\left(\begin{array}[]{c}\frac{2\left(2\left(D^{2}-5D+2\right)M_{3}^{2}+4(3D-2)M_{1}^{2}+2(2-3D)DM_{2}^{2}\right)}{(D-1)(3D-2)}\\\
-\frac{2\left(2D^{2}-9D+6\right)}{3D^{2}-5D+2}\\\
-\frac{2\left(2D^{2}-9D+6\right)}{3D^{2}-5D+2}\\\
\frac{2\left(2D^{2}-9D+6\right)}{3D^{2}-5D+2}\\\ \end{array}\right)\,,$ (357)
$\displaystyle\left(\begin{array}[]{c}C^{(1,2)}_{1}\\\ C^{(1,2)}_{5}\\\
C^{(1,2)}_{6}\\\
C^{(1,2)}_{7}\end{array}\right)\Bigg{|}_{s_{01}s_{0^{\prime}1}^{2}s_{11}^{-1}}$
$\displaystyle=\left(\begin{array}[]{c}\frac{2\left(2\left(D^{2}-5D+2\right)M_{3}^{2}+2(2-3D)DM_{1}^{2}+4(3D-2)M_{2}^{2}\right)}{(D-1)(3D-2)}\\\
-\frac{2\left(2D^{2}-9D+6\right)}{3D^{2}-5D+2}\\\
-\frac{2\left(2D^{2}-9D+6\right)}{3D^{2}-5D+2}\\\
\frac{2\left(2D^{2}-9D+6\right)}{3D^{2}-5D+2}\\\ \end{array}\right)\,,$ (366)
$\displaystyle\left(\begin{array}[]{c}C^{(0,3)}_{1}\\\ C^{(0,3)}_{5}\\\
C^{(0,3)}_{6}\\\
C^{(0,3)}_{7}\end{array}\right)\Bigg{|}_{s_{0^{\prime}1}^{3}s_{11}^{-1}}$
$\displaystyle=\left(\begin{array}[]{c}-\frac{8(D+2)\left((3D-2)M_{2}^{2}+(1-2D)M_{3}^{2}\right)}{(D-1)(3D-2)}\\\
-\frac{2\left(5D^{2}+6D-8\right)}{3D^{2}-5D+2}\\\
\frac{8D^{2}-60D+40}{3D^{2}-5D+2}\\\
\frac{2\left(5D^{2}+6D-8\right)}{(D-1)(3D-2)}\\\ \end{array}\right)\,.$ (375)

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