Enhancing Equity Trading Models Corporate Macro Sentiment Abnormality

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Enhancing Equity

Trading Models with

Corporate &Macro Sentiment

Abnormality

By:

Peter Hafez

Director of Quantitative Research

RavenPack

October 2014

Contact the authors

This White Paper is not intended for trading purposes. The White Paper is not appropriate for the purposes of making a decision to carry out a transaction or trade. Nor does it provide any form o

advice (investment, tax, legal) amounting to investment advice, or make any recommendations regarding particular financial instruments, investments or products. RavenPack may discontinue

change the White Paper content at any time, without notice. RavenPack does not guarantee or warrant the accuracy, completeness or timeliness of the White Paper. For more detailed disclaim

please refer to the back cover of this document.

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RavenPack Quantitative Research 2014All Rights Reserved. No duplication or redistribution of this document without written consent

TABLE OF CONTENTS

Executive Summary 2

Introduction 3

Data Description 4

Indicator Methodology 5

Evaluating MSI 9

Conclusion 12

Appendix 13
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Executive Summary

The aim of this paper is to construct a set of indicators or factors that will help a portfolio

manager outperform both a passive buy-and-hold strategy and a price momentum strategy on

the S&P 500. Specifically, we create a set of macro indicators that capture sentiment

abnormality across six topics, namely business, economic, political, environmental, societal and

corporate. Overall, we find that employing our macro sentiment indicators significantly

improves the performance of traditional long-only or long/short models. We also find that

company-specific or corporate sentiment is the key performance driver over a 1-month horizon.

Both our long-only and long/short news-based strategies deliver Information Ratios (IRs)

of 1.45 since September 2007. In comparison, a buy-and-hold S&P 500 strategy and a

pure return-driven model deliver IRs of only 0.23 and 0.73, respectively.

In addition, employing these news sentiment indicators is particularly useful for

managing downside risk as our sentiment models reduce the maximum drawdown by

more than 70% on the S&P 500 compared to a buy & hold strategy.

SOURCE: RavenPack, October 2014

About RavenPack Data

RavenPack News Analytics (RPNA) provides real-time structured sentiment, relevance and novelty data for entities

and events detected in unstructured text published by reputable sources. Publishers include Dow Jones

Newswires, Barrons, the Wall Street Journal and over 19,000 other traditional and social media sites. Over 14

years of Dow Jones newswires archive and 7 years of historical data from web publications and blogs are available

for backtesting. RavenPack detects news and produces analytics data on over 34,000 listed stocks from the world's

equity markets, over 2,500 financially relevant organizations, 138,000 places, 150 currencies and 80 commodities.

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1. Introduction

One of the challenges for news analytics is to improve the performance of traditional

investment strategies. Hence, the aim of this paper is to construct a set of indicators that will

help a portfolio manager outperform both a passive buy-and-hold strategy and a price

momentum strategy on the S&P 500.

We will call this family of factors Macro Sentiment Indicators (MSIs)and they are designed to

capture the daily news sentiment of an economy across different topics, namely Corporate,

Business, Economy, Politics, Environment and Society.1We also look at more granular groups

within these topics. For example, in Business we look at credit ratings, in Economy with look at

employment, in Environment we look at natural disasters, in Politics we look at foreign

relations, or in Society we analyze civil unrest.

In this paper we construct these indicators for the United States and apply them to a monthly

directional prediction model on the S&P 500 index. This thematic approach allows us to isolate

from the noise in the large volumes of news out there, and provides insight as to which

themes are dominant at any given time.

Overall, we find strong evidence that tracking news sentiment at a topical level can help predict

forward returns of the S&P 500. We find news sentiment especially useful for managing

downside risk as our sentiment model reduces the maximum drawdown by more than 70% as

compared to a simple buy & hold strategy. Our monthly long-only or long/short strategies have

delivered Information Ratios of nearly 1.5 since September 2007 vs. 0.6-0.7 for our return-

driven benchmarks that follow the same model construction approach. In terms of annualized

returns, the long-only strategy has yielded 13.5% vs. 23.0% for the long/short strategy. While

corporate sentiment is found to be the key return driverof market direction, including macro

sentiment in our model further improves strategy performance.

In the following section, we provide a brief overview of the data used in this paper. In Section 3,

we present our sentiment indicator methodology and Section 4 evaluates our indicators in adynamic regression framework with the aim of predicting the direction of the US equity market.

Finally, in Section 5 we present our conclusions.

1These topics are the high-level groups in the RavenPack event taxonomy.


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2. Data Description

This research is based on the Full Edition of RavenPack News Analytics Version 4.0, which

combines the equities and global macro analytics from the Dow Jones, Web, and Press Release

Editions. The Dow Jones Edition analyzes relevant information from Dow Jones Newswires,

regional editions of the Wall Street Journal, Marketwatch and Barron's. The Web Edition

automatically processes content from more than 19,000 sources including industry and

business publishers, national and local news, blogs, regulatory filings, and government updates.

The PR Edition analyzes the news and information from 22 regulatory news feeds and press

release distribution networks, including exclusive content from PRNewswire, Canadian News

Wire, LSE Regulatory News Service, and others.

The MSIs are based on RavenPacks Event Sentiment Score (ESS), which is a granular score

between 0 and 100 that represents the entity-specific sentiment for one of the events definedin the RavenPack event taxonomy. The score is determined by systematically matching stories

typically categorized by financial experts as having short-term positive or negative financial or

economic impact.

We only consider entities receiving a RavenPack Relevance Scoreof 100. The Relevance Score

measures how important an entity is within any given news article and can range from 0 to 100,

where 100 is most relevant.

We also weight each ESS score included in the MSI with RavenPacks Global Event Novelty Score

(G_ENS). G_ENS is a measure between 0 and 100 that represents how "new" or novel a news

story is within a 24-hour time window across all news providers covered by RavenPack2. Hence

breaking news gets most weight and subsequent news coverage less.

Finally, as previously mentioned, we utilize RavenPacks Event Taxonomyto isolate the different

potential drivers of overall market sentiment. The taxonomy identifies four levels of

classification, including event categories, types, groups, and topics, see Figure 1. It offers a

balance between levels of information aggregation, for those who look at news from the topdown, and granularity, for those who look at news from the bottom up. For this particular

study, we divide the business topic into two to create a sixth topic focusing purely on

company-specific events. We label this topic Corporate.

2For more details on Relevance, ESS, or G_ENS data please refer to Appendix A.


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Fig 1: The Structure of RavenPacks Event Taxonomy

This figure presents the structure of the RavenPack Event Taxonomy.

SOURCE: RavenPack, October 2014

3. Indicator Methodology

As previously mentioned, the aim of our paper is to create a family of Macro Sentiment

Indicators (MSIs) that capture the sentiment of an economy at the event topic and group levels.

At the highest level, we measure sentiment across the 6 topics: Corporate3, Business, Economy,

Politics, Environment and Society. The indicators are constructed as follows:

Step 1:Create a daily sentiment score at the event group level by taking the cumulative sum of

all event sentiment scores on a given day for an economy (e.g. the United States). ESS is

rescaled to take values between -1 and 1, and is weighted according to the novelty score of the

event.

Where is the event sentiment score for news event with representing the set ofnews events within a given event group on day . represents the global event noveltyscore for news event . We exclude events that dont contain sentiment (i.e. ) or thatmatch the entity type CMDT or CURR, since these events donot relate to any particular

country, but rather relate to commodities or currencies. In addition, we remove events with

entity type COMP from the macro package as we only want to model corporate news based

on the equities package.

3 Note that the corporate indicator is calculated from company-specific events spanning across all topics in the

RPNA Equities package.

Topics (5)

Groups (51)

Types (412)

Categories (2,064)


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Step 2:The daily scores, , are then normalized by country and per event group using a Z-scoreapproach. This allows us to measure sentiment abnormality within a given event group and

thereby identify key driversof current sentiment:

Where and are the 365 day average and standard deviation of .

Step 3:Create a daily topic level sentiment indicator, , by weighting the group level scoresaccording to their median ESS as observed over the past 365 days. The idea of using the median

ESS score over the last year is to come up with a systematic, non-price related weighting

function. With our suggested approach, we place more weight on event groups that generally

receive more extreme sentiment4:

Where is the median event sentiment score for event group withrepresenting theset of event groups belonging to the relevant Topicincluding Business, Economy, Environment,

Politics, Society and Corporate. Finally, is the normalized sentiment score for event groupon day .

3.1 Visual Index Representation

In Figures 2 and 3, we plot the 91-day moving average of corporate and economic sentiment

against the S&P 500 cumulative returns.5To provide further insight into what our sentiment

indicators are measuring, we look at some of the underlying news events driving extreme

sentiment. For example, we find that corporate sentiment deteriorated strongly during the

financial crisis primarily due to companies having fewer corporate actions, paying lower

dividends, less favorable price targets set by analysts, and increasingly filing for bankruptcy. On

the other hand, the later recovery seems to have been driven primarily by improved analyst

expectations, improvements to earnings and dividends, and fewer bankruptcies.

4Within each event group, more frequent events will have greater influence on the median score than less

frequent events.

5The remaining sentiment dimensions are showcased in Appendix B.


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Fig 2: The Corporate Sentiment Indicators (91-day Moving Average)

This figure presents the U.S. corporate sentiment indicator applying a 91-day moving average.

The indicator is plotted against the cumulative returns of the S&P 500. The evaluated period

covers January 2002 through August 2014. Web content is introduced from January 2008.

SOURCE: RavenPack, Yahoo Finance, October 2014

Looking at the Financial Crisis from an economic point-of-view, the deterioration in sentiment

was driven primarily by lower consumer confidence, a fall in economic growth and a general

increase in recession-guidance. Higher employment and improved economic growth guidance

have contributed to the recent recovery.

Focusing on sentiment since 2013, we can observe that both corporate and economic

sentiment doesnt seem to support, at least visually, the continued strong market performance

realized over the last couple of years. But this is difficult to judge with the naked eye so weproceed to test this in our predictive model in Section 3.

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corporate S&P 500 (secondary axis)

Rise in bankruptcies, price

target downgrades, lower

dividends, and less corporate

equity actions

Rise in business contracts,

product releases, and more

favorable analyst ratings

More favorable analyst ratings and

price-targets, improvements in earnings

and dividends, fewer bankruptcies

Less favorable analyst ratings

and price-targets and more

bankruptcy filings


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Fig 3: The Economic Sentiment Indicators (91-day Moving Average)

This figure presents the U.S. economic sentiment indicator applying a 91-day moving average.

The indicator is plotted against the cumulative returns of the S&P 500. The evaluated period

covers January 2002 through August 2014. Web content is introduced from January 2008.


4. Evaluating MSI

To evaluate the predictive power of our MSIs, we construct a simple rolling linear regression

model considering a monthly prediction horizon.6To benchmark the resulting strategies, we

create a simple buy-and-hold S&P 500 strategy as well as a pure price-driven momentum

strategy, including both a long-only and a long/short version.

Given our aim to predict the direction of the S&P 500 index, we create two sentiment-driven

models: 1) considering only corporate sentiment as we assume equity markets are primarily

driven by constituents news; and 2) a complete model including both corporate and macro

sentiment as well as market returns.

6The results of a weekly model are included in Appendix C.

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economy S&P 500 (secondary axis)

Higher interest rates

(actual) and (expected),less favorable GDP outlook

Lower consumer confidence

and economic growth, increasein recession expectations

Strong economic growth

guidance and higher

employment

Negative economic

growth guidance and

higher interest rateexpectations

Lower interest rate guidance, higher

economic growth, consumer

confidence and retail-sales


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4.1 Model description

For model estimation, considering the complete model, we include 7 sets of variables for

Business, Economic, Environment, Politics, Society, Corporate and S&P 500 returns. To allow for

a lead-lag relationship between sentiment and price, we include up to six lagged monthly

variables for each of our indicators.7

Note that each variable is created by taking the averagetopic sentiment score over the last 1-month horizon. The structure of our model is as follows:

With representing the S&P 500 return at time and representing the set of sentimentvariables with , while represents the number of lagged values included in the model.

The final model is selected using a stepwise Akaike Information Criterion (AIC) procedure with

bidirectional elimination. For each monthly step8, an automatic variable selecting is made and a

forward one period return prediction is made. To fit our model, we use the latest 5 years of

data as part of our estimation window - leaving us with 60 monthly data points, and up to 42

potential predictor variables. When evaluating the return-driven and corporate news strategies

only 6 variables are includedrepresenting the lagged monthly values.

4.2 Strategy Performance

When constructing our investment strategies, we hold a long position in the S&P 500 when our

1-month forward prediction is positive; while for the long-only and long/short strategies we

either stay in cash, yielding zero interest, or go short when a negative prediction is generated.

The performance of the different strategies is included in Figure 4 assuming zero transaction

costs. Over the back-testing period (September 2007 through August 2014), we find that both

the complete model (corporate & macro news + return), including all sentiment dimensions and

past returns, and the corporate news model, outperform a buy-and-hold S&P 500 strategy.

The sentiment strategies also significantly outperform the return-driven models. Even thoughwe find most of the performance is captured by corporate sentiment, the additional macro

variables improve the performanceincreasing the Information Ratio from 1.15 to 1.46 for the

long-only model and from 1.36 to 1.45 for the long/short model. This is compared to 0.23 for

7For the weekly model in Appendix C, we allow up to 12 lags of each predictor variable with sentiment measured

as an average of the past 1-week.

8The model is estimated 30 min before market closeassuming that we can enter the market at the close price.


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the buy-and-hold strategy and 0.73 for the return-based model. From an annualized return

perspective, the sentiment-based strategies yield about 13.5% and over 20% for the long-only

and long/short strategies, respectively.

Fig 4: Performance StatisticsPredicting S&P 500 Returns (Monthly Model)Long-only Monthly Model (6 lags)

Stat S&P500

Return-

driven

Corporate

News

Corporate&Macro

News + Return

Ann. Return 4.0% 7.7% 13.6% 13.5%

Ann. Volatility 17.2% 10.6% 11.8% 9.2%

Information Ratio 0.23 0.73 1.15 1.46

Max. Drawdown 54.8% 10.6% 11.1% 7.1%

Max. Recovery Period 72 20 8 17

L/S Monthly Model (6 lags)

Stat S&P500

Return-

driven

Corporate

News

Corporate&Macro

News + Return

Ann. Return 4.0% 10.0% 20.9% 23.0%

Ann. Volatility 17.2% 16.6% 15.3% 15.9%


Max. Drawdown 54.8% 15.6% 9.5% 15.3%


This figure presents the performance statistics of a long-only (top) and long/short (bottom)

strategy with monthly investment horizons . The S&P 500 is a buy-and-hold strategy, while the

return-driven and corporate news strategies include S&P 500 returns or corporate sentiment

only as predictor variables. The corporate&macro news + return model includes all sentimentindicators as well as S&P 500 returns ( 6 lags). The evaluated period covers September 2007

through August 2014.


The cumulative return profiles of the long-only and long/short strategies are included in Figure

5.


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Fig 5: Cumulative Return: Long-only Strategy (Monthly Model)

This figure presents the cumulative return of a long-only (top) and long/short (bottom) strategy

with monthly investment horizons . The S&P 500 is a buy-and-hold strategy, while the return-

driven and corporate news strategies include S&P 500 returns or corporate sentiment only aspredictor variables. The corporate&macro news + return model includes all sentiment indicators

as well as S&P 500 returns ( 6 lags). The evaluated period covers September 2007 through August

2014.


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When evaluating our strategies, we find that the number of included lags has an impact on the

overall strategy performance especially for the complete model. For instance, if we restrict

the number of lags to include only the past 3 months, the Information Ratio drops to about half

for the complete model (see Appendix D), while the corporate news model seems more stable -

with only a small drop to about 1.10. One explanation for this might be that corporate newstypically gets absorbed into equity prices faster than macro news, which may require a stronger

longer-term trend to emerge before impacting prices. This seems to be supported by our

weekly model results in Appendix C, where including macro variables only improves the

maximum drawdown profile and not the overall Information Ratio. Out of the 21 and 42

potential predictor variables for the 3-lag and 6-lag monthly models, on average only 9 and 24

variables survive the monthly step-wise AIC procedure.

5. Conclusion

We set out to create a set of macro sentiment indicators that captured sentiment abnormality

across 6 different news topics for an economy, namely business, economic, political,

environmental, societal and corporate. Employing these indicators in long-only and long/short

strategies on the S&P 500 we were able to generate additional value over traditional price-

driven models.

Aggregating company-specific sentiment into a macro corporate indicator is found to be the key

performance driver with noticeably stable results. Adding economic and geopolitical sentiment

indicators further improves the Information Ratio of our monthly model. Our news-based

models are able to achieve Information Ratios of around 1.45 for both a long-only and a

long/short strategy over our backtesting period (September 2007 through August 2014), while

the buy-and-hold S&P 500 strategy and the pure return-driven models only delivered

Information Ratios of 0.23 and 0.73, respectively. Besides the overall performance

improvements, we find news sentiment can be especially useful for managing downside risk as

our sentiment models reduce the maximum drawdown by more than 70% as compared to a

buy & hold strategy on the S&P 500.

In future research, we plan to explore the use of our macro sentiment indicators in other asset

classes including commodities, foreign exchange and fixed income. In addition, we plan to roll

out our sentiment index methodology across other countries and focus on sectors to address

the asset management challenges of constructing country and sector rotation models.


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ESS leverages RavenPacks event detection technology and produces an entity specific

sentiment score every time an event category is matched. ESS is based on RavenPack's Expert

Consensus and Event Score Factors methodologies.

RELEVANCE

A score between 0-100 that indicates how strongly related the entity is to the underlying news

story, with higher values indicating greater relevance. For any news story that mentions an

entity, RavenPack provides a relevance score. A score of 0 means the entity was passively

mentioned while a score of 100 means the entity was predominant in the news story. Values

above 75 are considered significantly relevant. Specifically, a value of 100 indicates that the

entity identified plays a key role in the news story and is considered highly relevant.

RavenPacks analysis is not limited to keywords or mentions when calculating relevance.

Automated classifiers look for meaning by detecting the roles entities play in specific events like

acquisitions or legal disputes or when announcing corporate actions, executive changes,

product launches or recalls, among many other categories. An entity will be assigned a high

mark of 100 if it plays a main role in these types of stories (context-aware). If an entity is

referenced in the headline or story body, it will receive a value between 0 and 99 (context-

unaware). The score is assigned by a proprietary text positioning algorithm based on where the

entity is first mentioned (i.e. headline, first paragraph, second paragraph, etc.), the number of

references in the text, and the overall number of entities mentioned in the story. Usually, a

relevance value of at least 90 indicates that the entity is referenced in the main title or headlineof the news item, while lower values indicate references further down the story body.

G_ENSGLOBAL EVENT NOVELTY SCORE

A score between 0 and 100 that represents how "new" or novel a news story is within a 24-

hour time window across all news providers covered by RavenPack. The first story reporting a

categorized event about one or more entities is considered to be the most novel and receives a

score of 100. Subsequent stories from any news provider covered by RavenPack about the

same event for the same entities receive scores following a decay function whose values are

(100 75 56 42 32 24 18 13 10 8 6 4 3 2 2 1 1 1 1 0 ...) based on the number of stories in the past

24 hour window. If a news story is published more than 24 hours after any other similar story, it

will again be considered novel and start a separate chain with a score of 100. Note that for any

particular story, the G_ENS score is based on the number of similar stories in the most recent

24-hour window preceding that story across all news providers covered by RavenPack.


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Appendix B:

The business sentiment indicatorcaptures information at a macro level concerning changes to

general supply and demand, products and services, business contracts, credit-ratings, industrial

accidents, regulatory approvals, labor-issues, and technical signals, among others.

Fig 6: The Business Sentiment Indicator (91-day Moving Average)

This figure presents the U.S. business sentiment indicator applying a 91-day moving average. The

indicator is plotted against the cumulative returns of the S&P 500. The evaluated period covers

January 2002 through August 2014. Web content is introduced from January 2008.


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(Sentiment)

business S&P 500 (secondary axis)

Increased investments, more

government contracts, and

general improvement to

supply/demand activity


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The politics sentiment indicator captures information at a macro level concerning elections,

foreign-relations, public-opinion, and changes to government.

Fig 7: The Political Sentiment Indicator (91-day Moving Average)

This figure presents the U.S. political sentiment indicator applying a 91-day moving average. The




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politics S&P 500 (secondary axis)

Obamas approval

rating drops below

50%

Obamas approval

rating soars

Obamas approvalrating rises

(Highest point in 3 years)

Obamas approval

rating drops (flawed

healthcare rollout)

Bushs approval

rating at all time lowBushs approval

rating keeps

dropping

Bushs approval

rating up

Obamas approval

rating plummets

on Jobs Plan


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The environmental sentiment indicator captures information at a macro level concerning

pollution and natural-disasters.

Fig 8: The Environmental Sentiment Indicator (91-day Moving Average)

This figure presents the U.S. environmental sentiment indicator applying a 91-day moving

average. The indicator is plotted against the cumulative returns of the S&P 500. The evaluated

period covers January 2002 through August 2014. Web content is introduced from January 2008.


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gAverage(Sentiment)

environment S&P 500 (secondary axis)

The Joplin tornado

(deadliest single U.S.

twister in more than

60 years)

Major

earthquake

in California

Hurricane

Katrina


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The societal sentiment indexcaptures information at a macro level concerning war and conflict,

civil-unrest, crime, health, security and legal issues.

Fig 9: The Societal Sentiment Indicator (91-day Moving Average)

This figure presents the U.S. societal sentiment indicator applying a 91-day moving average. The




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society S&P 500 (secondary axis)

Epidemic:

H1N1

(Swine flue)

Occupy Wall

Street protest U.S. menigitis

outbreak

Whooping

Cough

Epidemic

(California)

U.S. soldiers

and marines

killed in IraqU.S. soldiers

and marines

killed in Iraq

Hepatitis

Outbreak in

Pennsylvania


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Appendix C: Weekly Model Performance

When constructing our weekly investment strategy9, we hold a long position in the S&P 500

when our 1-week forward prediction is positive; while for the long-only and long/short strategy

we either stay in cash, yielding zero interest, or go short when a negative prediction is

generated. The performance of the different strategies is included in Figure 10 - assuming zerotransaction costs. Over our back-testing period, we find that both the complete model

(corporate & macro news + return), including all sentiment dimension and past returns, and the

corporate news model outperform a buy-and-hold S&P 500 strategy. Furthermore, the

sentiment strategy also significantly outperforms the return-driven models. Trading on a 1-

week horizon, we find that trading only on corporate sentiment outperforms the model

including macro variables, i.e. it seems that macro news doesnt addany additional predictive

power for short-term price movements.

Fig 10: Performance StatisticsPredicting S&P 500 Returns (Weekly Model)

Long-only Weekly Model (12 lags)

Stat S&P500

Return-

driven

Corporate

News

Corporate&Macro

News + Return

Ann. Return 5.6% 2.7% 9.8% 10.4%

Ann. Volatility 18.7% 15.7% 12.5% 13.1%


Max. Drawdown 60% 58% 19% 14%


L/S Weekly Model (12 lags)

Stat S&P500

Return-

driven

Corporate

News

Corporate&Macro

News + Return

Ann. Return 5.6% 0.4% 14.6% 15.2%

Ann. Volatility 18.7% 18.6% 18.5% 18.6%


Max. Drawdown 59.6% 59.0% 28.9% 16.5%


This figure presents the performance statistics of a long-only (top) and long/short (bottom)strategy with weekly investment horizons . The S&P 500 is a buy-and-hold strategy, while the


only as predictor variables. The corporate&macro news + return model includes all sentiment

indicators as well as S&P 500 returns ( 12 lags). The evaluated period covers September 2007

through August 2014.


9For the weekly model, 3 years of data is used for fitting - leaving 156 data points and 84 potential predictors.


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Appendix D: Monthly Model Performance (3-Lags)

Fig 12: Performance StatisticsPredicting S&P 500 Returns (Monthly Model)

Long-only Monthly Model (3 lags)

Stat S&P500Return-driven

CorporateNews

Corporate&MacroNews + Return

Ann. Return 3.6% 5.0% 11.3% 7.9%

Ann. Volatility 17.0% 10.3% 10.5% 11.4%


Max. Drawdown 54.8% 18.4% 9.5% 27.0%


L/S Monthly Model (3 lags)

Stat S&P500

Return-

driven

Corporate

News

Corporate&Macro

News + Return

Ann. Return 3.6% 4.9% 17.0% 12.2%

Ann. Volatility 17.0% 14.7% 15.3% 16.7%


Max. Drawdown 54.8% 21.5% 16.4% 18.6%


This figure presents the performance statistics of a long-only (top) and long/short (bottom)

strategy with monthly investment horizons . The S&P 500 is a buy-and-hold strategy, while the


only as predictor variables. The corporate&macro news + return model includes all sentiment

indicators as well as S&P 500 returns ( 3 lags). The evaluated period covers September 2007through August 2014.



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Enhancing Equity Trading Models Corporate Macro Sentiment Abnormality

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