This document summarizes an intraday event study using news data and market reaction profiles. Key points:
- It uses a unique real-time news feed classified with an extensive taxonomy to build intraday market reaction profiles for Russell 1000 stocks around news events.
- It defines abnormal returns, volatility, and volume around news events and finds distinct responses in these measures around news arrivals.
- By ranking profiles based on post-event returns, volatility, and event frequency, it aims to characterize market reactions and identify the most profitable trading strategies.
- Sentiment analysis and other news analytics like relevance and novelty are found to help filter significant effects and predict future price trends.
Financial quantitative strategies using artificial intelligenceStefan Duprey
Napoleon Crypto Asset Manager is developing an artificial intelligence-based allocation tool to optimize investment performance across its primary strategies for various asset classes including equities, rates, commodities, and cryptocurrencies. The tool will utilize a proprietary database of financial indicators and market states derived from raw market data through processing techniques. A supervised machine learning framework will be used to train algorithms on past periods to predict optimal allocations across strategies going forward, which will be backtested across all periods to evaluate performance out of sample. The goal is to find the configuration that delivers the best risk-adjusted returns through exhaustive testing of different algorithms and parameters.
This document discusses algorithmic trading and summarizes various techniques that can be used to develop and optimize automated trading systems including:
- Using technical analysis signals like MACD, RSI, and %R to characterize market state along with sentiment analysis of news data.
- Employing algorithms like genetic algorithms, neural networks, and random forests to improve trading strategies.
- Predicting order book evolution by analyzing customized market data from Reuters including order book depth and price/volume information over time.
- Applying machine learning techniques like regression trees, bagging, and genetic programming to build and optimize trading algorithms.
The document discusses quant's business cycle fund strategy. It begins with an overview of how business cycles are influenced by various macroeconomic factors and real economic indicators. It then discusses how quant uses tools like money flow analytics and their VLRT framework to identify investment opportunities across sectors that are well-positioned given the current stage of the business cycle. The strategy aims to generate superior risk-adjusted returns by taking a flexible approach to sector allocation and focusing on businesses set to benefit from changing cycles.
This document discusses incorporating news analysis into investment processes. It describes how news flows can be used to improve short-term risk assessments and condition risk estimates. Various data vendors that provide news analytics are also mentioned, as well as strategies for exploiting news signals, such as responding differently to "good" and "bad" news. Challenges with news-based strategies include defining events, assessing informational content, and managing holding periods.
A predictive system called "INSIGHT" was built using structured and unstructured data from various sources to identify potential buyers and sellers for large block trades. The system analyzed data like daily block trades, shareholder patterns, holdings, and market news to predict fund behavior. Additionally, the author learned quantitative research including technical analysis, building pair trading strategies, and value-at-risk models to analyze stock predictions and portfolio risk. The internship provided valuable experience in institutional equities trading and quantitative analysis techniques.
This document describes research on designing enhanced exchange traded funds (ETFs) that incorporate social media sentiment data from Twitter. The researchers use Twitter sentiment scores (S-Scores) from Social Market Analytics to strategically rebalance portions of existing ETFs tracking the S&P 500 (SPY), health care sector (XLV), and consumer discretionary sector (XLY). Backtesting of rebalancing the highest market capitalization stocks in the ETFs based on dynamic confidence intervals of S-Scores shows the sentiment-enhanced ETFs yield better returns than the original benchmarks.
This document summarizes a final year project that created a framework for sentiment analysis of financial markets. The framework collects stock market data like prices and volumes alongside sentiment indicators from news and social media. Natural language processing techniques like sentiment analysis and named entity recognition are used to analyze unstructured text data. The project developed tools for these tasks as well as an interface to display results. While not attempting to predict markets, the framework provides a proof of concept for using sentiment analysis in financial applications. Future work could improve the sentiment analysis model and expand data collection and processing capabilities.
Analytics, business cycles and disruptionsMark Albala
The digital economy is different. Depending on platforms and a much more malleable set of methods to interact with consumers, an accelerated rate of disruptions compromises the orderly business experience of most market participants. A well-honed analytics program facilitates understanding these accelerated disruptions. With a platform based digital marketplace, obtaining the information necessary to decipher unexpected outcomes and prescribe suitable actions is difficult because the information required Both of these facts are important to analytics. First, platforms. Platform based activity is hard to decipher, not because it is more complex but because the information needed to decipher activity is not contained within your four walls.
Once deciphered, the next challenge facing organizations deciphering unexpected outcomes is a determination of whether the unexpected outcome is truly a disruptive event or simply a phase change in a regularly occurring business cycle. There are significant differences in the suitable reactions to disruptions and business cycle phase changes. Unfortunately, many organizations are ill equipped to discern between these two classes of unexpected business outcomes and consistently find their business plans fall victim to the actions of others within the marketplace.
Luckily, many of the activities of governmental and regulatory bodies are focused on predicting phase changes to the business cycles likely to impact the economic forces within the next fiscal year and describe their economic policies and agendas in publicly available documents and analysis. Understanding where to find these documents and how to use the published to discern between the likely business cycle phase changes and true disruptions as one of the vehicles available within your arsenal of analytics will lessen the occurrence of falling victim in the marketplace by misreading the clues available from unexpected outcomes. This document will address the sources most likely to assist and the actions to be taken to utilize the information attained from these documents.
Financial quantitative strategies using artificial intelligenceStefan Duprey
Napoleon Crypto Asset Manager is developing an artificial intelligence-based allocation tool to optimize investment performance across its primary strategies for various asset classes including equities, rates, commodities, and cryptocurrencies. The tool will utilize a proprietary database of financial indicators and market states derived from raw market data through processing techniques. A supervised machine learning framework will be used to train algorithms on past periods to predict optimal allocations across strategies going forward, which will be backtested across all periods to evaluate performance out of sample. The goal is to find the configuration that delivers the best risk-adjusted returns through exhaustive testing of different algorithms and parameters.
This document discusses algorithmic trading and summarizes various techniques that can be used to develop and optimize automated trading systems including:
- Using technical analysis signals like MACD, RSI, and %R to characterize market state along with sentiment analysis of news data.
- Employing algorithms like genetic algorithms, neural networks, and random forests to improve trading strategies.
- Predicting order book evolution by analyzing customized market data from Reuters including order book depth and price/volume information over time.
- Applying machine learning techniques like regression trees, bagging, and genetic programming to build and optimize trading algorithms.
The document discusses quant's business cycle fund strategy. It begins with an overview of how business cycles are influenced by various macroeconomic factors and real economic indicators. It then discusses how quant uses tools like money flow analytics and their VLRT framework to identify investment opportunities across sectors that are well-positioned given the current stage of the business cycle. The strategy aims to generate superior risk-adjusted returns by taking a flexible approach to sector allocation and focusing on businesses set to benefit from changing cycles.
This document discusses incorporating news analysis into investment processes. It describes how news flows can be used to improve short-term risk assessments and condition risk estimates. Various data vendors that provide news analytics are also mentioned, as well as strategies for exploiting news signals, such as responding differently to "good" and "bad" news. Challenges with news-based strategies include defining events, assessing informational content, and managing holding periods.
A predictive system called "INSIGHT" was built using structured and unstructured data from various sources to identify potential buyers and sellers for large block trades. The system analyzed data like daily block trades, shareholder patterns, holdings, and market news to predict fund behavior. Additionally, the author learned quantitative research including technical analysis, building pair trading strategies, and value-at-risk models to analyze stock predictions and portfolio risk. The internship provided valuable experience in institutional equities trading and quantitative analysis techniques.
This document describes research on designing enhanced exchange traded funds (ETFs) that incorporate social media sentiment data from Twitter. The researchers use Twitter sentiment scores (S-Scores) from Social Market Analytics to strategically rebalance portions of existing ETFs tracking the S&P 500 (SPY), health care sector (XLV), and consumer discretionary sector (XLY). Backtesting of rebalancing the highest market capitalization stocks in the ETFs based on dynamic confidence intervals of S-Scores shows the sentiment-enhanced ETFs yield better returns than the original benchmarks.
This document summarizes a final year project that created a framework for sentiment analysis of financial markets. The framework collects stock market data like prices and volumes alongside sentiment indicators from news and social media. Natural language processing techniques like sentiment analysis and named entity recognition are used to analyze unstructured text data. The project developed tools for these tasks as well as an interface to display results. While not attempting to predict markets, the framework provides a proof of concept for using sentiment analysis in financial applications. Future work could improve the sentiment analysis model and expand data collection and processing capabilities.
Analytics, business cycles and disruptionsMark Albala
The digital economy is different. Depending on platforms and a much more malleable set of methods to interact with consumers, an accelerated rate of disruptions compromises the orderly business experience of most market participants. A well-honed analytics program facilitates understanding these accelerated disruptions. With a platform based digital marketplace, obtaining the information necessary to decipher unexpected outcomes and prescribe suitable actions is difficult because the information required Both of these facts are important to analytics. First, platforms. Platform based activity is hard to decipher, not because it is more complex but because the information needed to decipher activity is not contained within your four walls.
Once deciphered, the next challenge facing organizations deciphering unexpected outcomes is a determination of whether the unexpected outcome is truly a disruptive event or simply a phase change in a regularly occurring business cycle. There are significant differences in the suitable reactions to disruptions and business cycle phase changes. Unfortunately, many organizations are ill equipped to discern between these two classes of unexpected business outcomes and consistently find their business plans fall victim to the actions of others within the marketplace.
Luckily, many of the activities of governmental and regulatory bodies are focused on predicting phase changes to the business cycles likely to impact the economic forces within the next fiscal year and describe their economic policies and agendas in publicly available documents and analysis. Understanding where to find these documents and how to use the published to discern between the likely business cycle phase changes and true disruptions as one of the vehicles available within your arsenal of analytics will lessen the occurrence of falling victim in the marketplace by misreading the clues available from unexpected outcomes. This document will address the sources most likely to assist and the actions to be taken to utilize the information attained from these documents.
This document summarizes research on the momentum factor in equities. It finds that stocks with strong recent performance tend to continue outperforming, known as the momentum effect. The biggest challenge for capturing momentum is its high inherent turnover. Using optimization in portfolio construction can successfully capture momentum while controlling turnover. Adding momentum to portfolios with other factors like value provides diversification benefits due to its negative correlation with value.
IRJET- Stock Market Prediction using ANNIRJET Journal
This document summarizes a research paper that aims to predict stock prices using artificial neural networks (ANN). It discusses using factors like moving averages, stochastic oscillator, standard deviation, and on-balance volume as inputs to an ANN model to predict stock prices. The motivation is to help investors make better investment decisions. ANN is chosen because it can model nonlinear relationships better than linear models. Prior literature shows ANN models have achieved higher accuracy than support vector machine or linear regression models for stock price prediction. The methodology section describes the technical indicators used as inputs and the multi-layer perceptron ANN algorithm with backpropagation that is implemented.
This document summarizes a statistical arbitrage strategy that evaluates mean reversion in stock prices over time. It describes the strategy's assumptions that stock prices temporarily diverge from their equilibrium relative to the market before reverting. The experiment uses S&P 500 stock data to calculate daily returns, correlations, betas and residuals over rolling 60-day windows. When residuals exceed +/-2 standard deviations, positions are taken assuming reversion will occur. While backtested returns are appealing, live trading realities like transaction costs and limited share availability would likely reduce profits versus this theoretical analysis.
IRJET - Forecasting Stock Market Movement Direction using Sentiment Analysis ...IRJET Journal
This document describes a study that uses sentiment analysis and support vector machines to forecast the direction of stock market movement in China. It constructs sentiment indexes from financial news and social media texts, accounting for the day-of-week effect. Support vector machines are used to predict movement of the SSE 50 Index, achieving up to 89.93% accuracy when combining sentiment features with market data. The findings imply that sentiment contains valuable information for predicting stock prices and market trends.
This document describes a study examining the "smart money effect" by including a new cash flow factor in the Fama-French three-factor model. The authors include a cash flow factor called PMN, representing the difference between positive and negative cash flow portfolios. They use regression to select funds with positive alpha values to form two portfolios based on the original three-factor model and new four-factor model including PMN. They then use Markowitz optimization to analyze the portfolios and compare results. The goal is to test if cash flow influences returns and if successful fund managers rely on the influence of cash flow.
Nowadays during increasingly developed technology of the World Wide Web and Internet, the data is becoming extremely rich. With the application of data recognition process, the information extracted from data has become the most important part in some areas of society, management field, finance and markets, etc. It is necessary to develop the valid method to understand the knowledge of the data. Whether you are looking for good investments or are into stock trading, stock prediction or forecast plays the most crucial role in determining where to put in the money or which stock to be acquired or sold.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
- The document analyzes whether using Markit's dividend forecasting data to create portfolios based on forward dividend yield leads to higher returns compared to portfolios based on historical trailing dividend yield or benchmarks.
- A portfolio based on Markit's high forward dividend yield (HFDY) outperformed a similar portfolio based on trailing yield (HTDY) and the S&P High Yield Dividend Aristocrats index in terms of total returns over the sample period.
- HFDY had higher annual returns and information ratio, indicating it generated more return for the risk taken compared to the other strategies. However, the SPDA benchmark had the lowest volatility and highest information ratio of the strategies analyzed.
The document discusses a 2008 journal article that examines how quantifying language in news stories can predict firms' fundamentals and stock returns. It finds that a higher frequency of negative words forecasts lower earnings and brief underreactions in stock prices that correct over time. Words related to fundamentals have the highest predictability. The findings suggest investors quickly incorporate qualitative information from news into stock prices.
This document is a thesis submitted by Abdul Rahim Wong to IICSE University in partial fulfillment of the requirements for a PhD in Business Administration. The thesis examines using mathematical vectors in financial graphs as a new quantitative analysis tool. It provides an extensive literature review on relevant topics like financial modeling, quantitative analysis, technical analysis, statistics, and different types of financial charts and indicators. The literature review establishes the need to develop new quantitative tools to help analyze financial data and make investment decisions.
This document is a thesis submitted by Abdul Rahim Wong for a PhD in Business Administration. The thesis proposes using mathematical vectors in financial graphs as a new quantitative analysis tool. It provides an introduction to the topic and reviews literature on financial modeling, quantitative analysis, and technical analysis. Key assumptions of technical analysis are discussed, including that the security has high liquidity, no artificial price changes, and no extreme news influencing price. Different types of financial charts and indicators are also summarized, including candlestick charts.
This document is a thesis submitted by Abdul Rahim Wong to IICSE University in partial fulfillment of the requirements for a PhD in Business Administration. The thesis examines using mathematical vectors in financial graphs as a new quantitative analysis tool. It provides an extensive literature review on relevant topics like financial modeling, quantitative analysis, technical analysis, statistics, and different types of financial charts and indicators. The literature review establishes the necessary background and lays the groundwork for exploring how vectors could be applied as a new indicator in financial data analysis.
This document is a thesis submitted by Abdul Rahim Wong to IICSE University in partial fulfillment of the requirements for a PhD in Business Administration. The thesis examines using mathematical vectors in financial graphs as a new quantitative analysis tool. It provides an extensive literature review on relevant topics like financial modeling, quantitative analysis, technical analysis, statistics, and different types of financial charts and indicators. The literature review establishes the foundation for exploring how vectors can be applied as a new indicator in financial data analysis.
This document is a thesis submitted by Abdul Rahim Wong to IICSE University in partial fulfillment of the requirements for a PhD in Business Administration. The thesis examines using mathematical vectors in financial graphs as a new quantitative analysis tool. It provides an extensive literature review on relevant topics like financial modeling, quantitative analysis, technical analysis, statistics, and different types of financial charts and indicators. The literature review establishes the need to explore new tools for financial data analysis and evaluates how vectors could potentially be applied as an indicator in financial displays.
The document discusses predicting Google stock prices using machine learning algorithms. It explores using news sentiment data and historical stock prices to build regression and time series forecasting models. Multiple linear regression, ARIMA, and Holt-Winters exponential smoothing models were tested on the data, with ARIMA producing the best results with an RMSE of 123.73 on test data. The aim is to improve predictive accuracy of Google stock movements.
Data Mining refers to the analysis of large amounts of data stored in computers. The Big Data
era is already present, with current sources indicating that more data have been created over the last two years
than they have been generated throughout the entire human history. Big Data involves data sets so large that
traditional data analysis methods are no longer usable due to the huge amount of data. Lacking or ignoring
the data structure is an extremely important aspect,
This document is a capstone project report analyzing the Australia grocery market which is dominated by Woolworth and Coles with over 60% combined market share. The report includes an introduction outlining the research topic and background. It then provides a literature review on relevant anti-combination laws, the effects of monopoly, and risk analysis. The report also describes the data collection and analysis methodology to be used. It will analyze factors related to the grocery industry and identify hypotheses about whether the current monopoly is optimal for the Australian market and consumers. The conclusion will forecast if other competitors could become major players by utilizing appropriate short- and long-term strategies.
This document introduces the Two Sigma Factor Lens, which is a framework for constructing a parsimonious set of risk factors that individually describe independent risks across many asset classes yet collectively explain much of the risk in typical institutional investor portfolios. The lens is intended to capture the majority of risk in a holistic yet concise manner so that changes to factor exposures can easily translate to asset allocation changes. The document discusses how analyzing portfolios through a risk factor lens allows investors to better understand overlapping risk sources across asset classes and more efficiently manage portfolio risk.
Sentiment Analysis based Stock Forecast ApplicationIRJET Journal
This document discusses a research paper that proposes a sentiment analysis based stock forecasting application. It uses sentiment analysis techniques on financial news, social media, and other textual data to analyze market sentiment and predict stock price trends. The proposed system uses two datasets - one from Kaggle containing financial news data and another real-time dataset from Finviz and yfinance. It applies several machine learning classifiers for sentiment analysis, including Decision Tree, Logistic Regression, Naive Bayes, and LSTM. The goal is to develop an accurate stock prediction model using sentiment data as an additional input to traditional quantitative methods.
OPENING RANGE BREAKOUT STOCK TRADING ALGORITHMIC MODELIJCI JOURNAL
Stock Trading Algorithmic Model is an important research problem that is dealt with knowledge in
fundamental and technical analysis, combined with the knowledge expertise in programming and computer
science. There have been numerous attempts in predicting stock trends, we aim to predict it with least
amount of computation and to decrease the space complexity. The goal of this paper is to create a hybrid
recommendation system that will inform the trader about the future of a stock trend in order to improve the
profitability of a short term investment. We make use of technical analysis tools to incorporate this
recommendation into our system. In order to understand the results, we implemented a prototype in R
programming language.
Dynamical smart liquidity on decentralized exchanges for lucrative market makingStefan Duprey
This document discusses liquidity provision on Uniswap V3 and proposes algorithms for actively managing liquidity positions. It introduces the concept of concentrated liquidity on Uniswap V3 which allows liquidity providers to specify a price range. It discusses different configurations for liquidity provision including long, short, and market neutral approaches. It then presents algorithms for dynamically exiting liquidity positions based on trend signals, modeling historical fees, and determining optimal price range bounds while minimizing costs like impermanent loss and transaction fees.
This document discusses two strategies for generating yield from bitcoin holdings:
1. A low frequency strategy that sells in-the-money covered calls with long durations of over 6 months, generating a large premium but capping upside gains.
2. A middle frequency strategy that rolls call options weekly with strikes unlikely to be reached, compounding the premium over time.
It also discusses optimizing call option maturities and strikes to guarantee a target yield percentage while allowing for varying degrees of upside potential.
This document summarizes research on the momentum factor in equities. It finds that stocks with strong recent performance tend to continue outperforming, known as the momentum effect. The biggest challenge for capturing momentum is its high inherent turnover. Using optimization in portfolio construction can successfully capture momentum while controlling turnover. Adding momentum to portfolios with other factors like value provides diversification benefits due to its negative correlation with value.
IRJET- Stock Market Prediction using ANNIRJET Journal
This document summarizes a research paper that aims to predict stock prices using artificial neural networks (ANN). It discusses using factors like moving averages, stochastic oscillator, standard deviation, and on-balance volume as inputs to an ANN model to predict stock prices. The motivation is to help investors make better investment decisions. ANN is chosen because it can model nonlinear relationships better than linear models. Prior literature shows ANN models have achieved higher accuracy than support vector machine or linear regression models for stock price prediction. The methodology section describes the technical indicators used as inputs and the multi-layer perceptron ANN algorithm with backpropagation that is implemented.
This document summarizes a statistical arbitrage strategy that evaluates mean reversion in stock prices over time. It describes the strategy's assumptions that stock prices temporarily diverge from their equilibrium relative to the market before reverting. The experiment uses S&P 500 stock data to calculate daily returns, correlations, betas and residuals over rolling 60-day windows. When residuals exceed +/-2 standard deviations, positions are taken assuming reversion will occur. While backtested returns are appealing, live trading realities like transaction costs and limited share availability would likely reduce profits versus this theoretical analysis.
IRJET - Forecasting Stock Market Movement Direction using Sentiment Analysis ...IRJET Journal
This document describes a study that uses sentiment analysis and support vector machines to forecast the direction of stock market movement in China. It constructs sentiment indexes from financial news and social media texts, accounting for the day-of-week effect. Support vector machines are used to predict movement of the SSE 50 Index, achieving up to 89.93% accuracy when combining sentiment features with market data. The findings imply that sentiment contains valuable information for predicting stock prices and market trends.
This document describes a study examining the "smart money effect" by including a new cash flow factor in the Fama-French three-factor model. The authors include a cash flow factor called PMN, representing the difference between positive and negative cash flow portfolios. They use regression to select funds with positive alpha values to form two portfolios based on the original three-factor model and new four-factor model including PMN. They then use Markowitz optimization to analyze the portfolios and compare results. The goal is to test if cash flow influences returns and if successful fund managers rely on the influence of cash flow.
Nowadays during increasingly developed technology of the World Wide Web and Internet, the data is becoming extremely rich. With the application of data recognition process, the information extracted from data has become the most important part in some areas of society, management field, finance and markets, etc. It is necessary to develop the valid method to understand the knowledge of the data. Whether you are looking for good investments or are into stock trading, stock prediction or forecast plays the most crucial role in determining where to put in the money or which stock to be acquired or sold.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
- The document analyzes whether using Markit's dividend forecasting data to create portfolios based on forward dividend yield leads to higher returns compared to portfolios based on historical trailing dividend yield or benchmarks.
- A portfolio based on Markit's high forward dividend yield (HFDY) outperformed a similar portfolio based on trailing yield (HTDY) and the S&P High Yield Dividend Aristocrats index in terms of total returns over the sample period.
- HFDY had higher annual returns and information ratio, indicating it generated more return for the risk taken compared to the other strategies. However, the SPDA benchmark had the lowest volatility and highest information ratio of the strategies analyzed.
The document discusses a 2008 journal article that examines how quantifying language in news stories can predict firms' fundamentals and stock returns. It finds that a higher frequency of negative words forecasts lower earnings and brief underreactions in stock prices that correct over time. Words related to fundamentals have the highest predictability. The findings suggest investors quickly incorporate qualitative information from news into stock prices.
This document is a thesis submitted by Abdul Rahim Wong to IICSE University in partial fulfillment of the requirements for a PhD in Business Administration. The thesis examines using mathematical vectors in financial graphs as a new quantitative analysis tool. It provides an extensive literature review on relevant topics like financial modeling, quantitative analysis, technical analysis, statistics, and different types of financial charts and indicators. The literature review establishes the need to develop new quantitative tools to help analyze financial data and make investment decisions.
This document is a thesis submitted by Abdul Rahim Wong for a PhD in Business Administration. The thesis proposes using mathematical vectors in financial graphs as a new quantitative analysis tool. It provides an introduction to the topic and reviews literature on financial modeling, quantitative analysis, and technical analysis. Key assumptions of technical analysis are discussed, including that the security has high liquidity, no artificial price changes, and no extreme news influencing price. Different types of financial charts and indicators are also summarized, including candlestick charts.
This document is a thesis submitted by Abdul Rahim Wong to IICSE University in partial fulfillment of the requirements for a PhD in Business Administration. The thesis examines using mathematical vectors in financial graphs as a new quantitative analysis tool. It provides an extensive literature review on relevant topics like financial modeling, quantitative analysis, technical analysis, statistics, and different types of financial charts and indicators. The literature review establishes the necessary background and lays the groundwork for exploring how vectors could be applied as a new indicator in financial data analysis.
This document is a thesis submitted by Abdul Rahim Wong to IICSE University in partial fulfillment of the requirements for a PhD in Business Administration. The thesis examines using mathematical vectors in financial graphs as a new quantitative analysis tool. It provides an extensive literature review on relevant topics like financial modeling, quantitative analysis, technical analysis, statistics, and different types of financial charts and indicators. The literature review establishes the foundation for exploring how vectors can be applied as a new indicator in financial data analysis.
This document is a thesis submitted by Abdul Rahim Wong to IICSE University in partial fulfillment of the requirements for a PhD in Business Administration. The thesis examines using mathematical vectors in financial graphs as a new quantitative analysis tool. It provides an extensive literature review on relevant topics like financial modeling, quantitative analysis, technical analysis, statistics, and different types of financial charts and indicators. The literature review establishes the need to explore new tools for financial data analysis and evaluates how vectors could potentially be applied as an indicator in financial displays.
The document discusses predicting Google stock prices using machine learning algorithms. It explores using news sentiment data and historical stock prices to build regression and time series forecasting models. Multiple linear regression, ARIMA, and Holt-Winters exponential smoothing models were tested on the data, with ARIMA producing the best results with an RMSE of 123.73 on test data. The aim is to improve predictive accuracy of Google stock movements.
Data Mining refers to the analysis of large amounts of data stored in computers. The Big Data
era is already present, with current sources indicating that more data have been created over the last two years
than they have been generated throughout the entire human history. Big Data involves data sets so large that
traditional data analysis methods are no longer usable due to the huge amount of data. Lacking or ignoring
the data structure is an extremely important aspect,
This document is a capstone project report analyzing the Australia grocery market which is dominated by Woolworth and Coles with over 60% combined market share. The report includes an introduction outlining the research topic and background. It then provides a literature review on relevant anti-combination laws, the effects of monopoly, and risk analysis. The report also describes the data collection and analysis methodology to be used. It will analyze factors related to the grocery industry and identify hypotheses about whether the current monopoly is optimal for the Australian market and consumers. The conclusion will forecast if other competitors could become major players by utilizing appropriate short- and long-term strategies.
This document introduces the Two Sigma Factor Lens, which is a framework for constructing a parsimonious set of risk factors that individually describe independent risks across many asset classes yet collectively explain much of the risk in typical institutional investor portfolios. The lens is intended to capture the majority of risk in a holistic yet concise manner so that changes to factor exposures can easily translate to asset allocation changes. The document discusses how analyzing portfolios through a risk factor lens allows investors to better understand overlapping risk sources across asset classes and more efficiently manage portfolio risk.
Sentiment Analysis based Stock Forecast ApplicationIRJET Journal
This document discusses a research paper that proposes a sentiment analysis based stock forecasting application. It uses sentiment analysis techniques on financial news, social media, and other textual data to analyze market sentiment and predict stock price trends. The proposed system uses two datasets - one from Kaggle containing financial news data and another real-time dataset from Finviz and yfinance. It applies several machine learning classifiers for sentiment analysis, including Decision Tree, Logistic Regression, Naive Bayes, and LSTM. The goal is to develop an accurate stock prediction model using sentiment data as an additional input to traditional quantitative methods.
OPENING RANGE BREAKOUT STOCK TRADING ALGORITHMIC MODELIJCI JOURNAL
Stock Trading Algorithmic Model is an important research problem that is dealt with knowledge in
fundamental and technical analysis, combined with the knowledge expertise in programming and computer
science. There have been numerous attempts in predicting stock trends, we aim to predict it with least
amount of computation and to decrease the space complexity. The goal of this paper is to create a hybrid
recommendation system that will inform the trader about the future of a stock trend in order to improve the
profitability of a short term investment. We make use of technical analysis tools to incorporate this
recommendation into our system. In order to understand the results, we implemented a prototype in R
programming language.
Dynamical smart liquidity on decentralized exchanges for lucrative market makingStefan Duprey
This document discusses liquidity provision on Uniswap V3 and proposes algorithms for actively managing liquidity positions. It introduces the concept of concentrated liquidity on Uniswap V3 which allows liquidity providers to specify a price range. It discusses different configurations for liquidity provision including long, short, and market neutral approaches. It then presents algorithms for dynamically exiting liquidity positions based on trend signals, modeling historical fees, and determining optimal price range bounds while minimizing costs like impermanent loss and transaction fees.
This document discusses two strategies for generating yield from bitcoin holdings:
1. A low frequency strategy that sells in-the-money covered calls with long durations of over 6 months, generating a large premium but capping upside gains.
2. A middle frequency strategy that rolls call options weekly with strikes unlikely to be reached, compounding the premium over time.
It also discusses optimizing call option maturities and strikes to guarantee a target yield percentage while allowing for varying degrees of upside potential.
Short Term Intraday Long Only Crypto StrategiesStefan Duprey
The document describes two short-term trading models for a spot market/long only strategy. Model 1 generates hourly oscillating signals for quick deleveraging, while Model 2 uses hourly RSI cutoffs from multiple timeframes. Backtests of Model 1 show average hourly turnover of 5.6% with 10 bps transaction costs included, and sensitivity analysis was performed by varying strategy parameters to assess robustness. Results for different parameter sets and the impact of transaction costs on profits are also presented.
On Chain Weekly Rebal Low Expo StrategyStefan Duprey
This strategy involves rebalancing a portfolio weekly that is 90% invested in stable coin farming and 10% split between long and short positions on other cryptocurrencies. The portfolio aims to benefit from both rising and falling prices of cryptocurrencies through the use of long and short positions while keeping the majority invested in stable coin yields.
This document proposes an optimal stable coin folding strategy on the Aave protocol to generate yield. It describes iterating a "lend/borrow" strategy multiple times, or "folding", to increase yields in a low-risk manner due to stable coins' pegging and low liquidation risk. The strategy involves allocating assets optimally across multiple Aave pools to maximize total APY. It presents a linear optimization approach to solve this allocation problem that can be implemented efficiently on-chain. Numerical examples demonstrate the non-linear and linearized solutions. Finally, the relevant smart contract code is outlined.
Curve pools multiple allocation fairness strategies to distribute funds deposited into its liquidity pools. It uses a mix of constant product and constant sum strategies to maintain a stable swap invariant across pools. When users deposit funds, Curve calculates the change in pool invariants and mints LP tokens proportionally. It aims to mitigate slippage for a meta strategy by optimizing the allocation of deposits between pools using numerical methods.
We here detail how to build through variance optimization a range of portfolio over a multi risk framework factor. This methodology is much used by practitioner as the factors covariance is non singular even with few observations and more stable.
Impact best bid/ask limit order executionStefan Duprey
The document discusses estimating the market impact of different types of limit orders using order book data from electronic exchanges like NASDAQ. It proposes using a cointegrated Vector AutoRegression model to analyze the limit order book dynamics and quantify the trading friction associated with strategies that use limit orders versus market orders. The analysis considers normal passive limit orders, aggressive limit orders, and normal market orders to understand how each impacts market liquidity and the order book.
The document discusses optimal strategies for executing portfolio transactions that balance liquidation costs and volatility risks. It presents a dilemma between trading everything now at a known high cost versus trading in small packets over time with lower liquidation costs but higher uncertainty in final revenue due to volatility. The document proposes modeling the problem as an optimization that minimizes expected shortfall and variance, allowing traders to assess their risk tolerance, and outlines assumptions and approaches for solving it either analytically or numerically.
This document summarizes a presentation on developing a natural finite element for axisymmetric problems. It introduces an axisymmetric model problem, defines appropriate axisymmetric Sobolev spaces, and presents a discrete formulation using a P1 finite element on triangles. Numerical results on a test problem show the method achieves the same convergence rates as classical approaches but with significantly smaller errors. The analysis draws on previous work to prove first-order approximation properties under certain mesh assumptions.
Page rank optimization to push successful URLs or products for e-commerceStefan Duprey
1. The document discusses different approaches for optimizing internal mesh structures on websites, including heuristics, metaheuristics like genetic algorithms, and shrinking the problem space by allowing only semantically similar links.
2. A genetic algorithm is proposed to optimize page rank and traffic potential across URLs to find optimal mesh structures, as there are many local optima.
3. The problem is framed for e-commerce sites by optimizing keywords based on search volume, click-through rate, and other metrics.
This document provides an overview of machine learning techniques that can be applied in finance, including exploratory data analysis, clustering, classification, and regression methods. It discusses statistical learning approaches like data mining and modeling. For clustering, it describes techniques like k-means clustering, hierarchical clustering, Gaussian mixture models, and self-organizing maps. For classification, it mentions discriminant analysis, decision trees, neural networks, and support vector machines. It also provides summaries of regression, ensemble methods, and working with big data and distributed learning.
Calculation of compliance cost: Veterinary and sanitary control of aquatic bi...Alexander Belyaev
Calculation of compliance cost in the fishing industry of Russia after extended SCM model (Veterinary and sanitary control of aquatic biological resources (ABR) - Preparation of documents, passing expertise)
An accounting information system (AIS) refers to tools and systems designed for the collection and display of accounting information so accountants and executives can make informed decisions.
Budgeting as a Control Tool in Government Accounting in Nigeria
Being a Paper Presented at the Nigerian Maritime Administration and Safety Agency (NIMASA) Budget Office Staff at Sojourner Hotel, GRA, Ikeja Lagos on Saturday 8th June, 2024.
How to Invest in Cryptocurrency for Beginners: A Complete GuideDaniel
Cryptocurrency is digital money that operates independently of a central authority, utilizing cryptography for security. Unlike traditional currencies issued by governments (fiat currencies), cryptocurrencies are decentralized and typically operate on a technology called blockchain. Each cryptocurrency transaction is recorded on a public ledger, ensuring transparency and security.
Cryptocurrencies can be used for various purposes, including online purchases, investment opportunities, and as a means of transferring value globally without the need for intermediaries like banks.
Navigating Your Financial Future: Comprehensive Planning with Mike Baumannmikebaumannfinancial
Learn how financial planner Mike Baumann helps individuals and families articulate their financial aspirations and develop tailored plans. This presentation delves into budgeting, investment strategies, retirement planning, tax optimization, and the importance of ongoing plan adjustments.
Independent Call Girls Visakhapatnam 8800000000 Low Rate HIgh Profile Visakha...
Intraday news event_study
1. Trading
Intraday : a
complete
intraday event
study
TABLE OF CONTENTS
Executive Summary 2
Introduction 3
Data Description 4
Methodology 6
Results 11
Conclusions 20
References 30
Contact
stefan.duprey@gmail.com
2. Quantitative Research
P a g e | 2
Executive Summary
In the race to generate alphas and avoid overcrowded standard factors, investors must identify
and extract value from every possible data source.
We show in this paper that news database is an alternative source of alpha apart from standard
fundamental data built factors.
In this study, we tackle the problematic of intraday trading reaction to news under the angle of
big data analytics for finance from Big Data analytics.
More specifically, we detail:
By using unique real time automated and pre-processed news data feed based on
linguistic pattern recognition we exploit entity detection and event taxonomy
classification to build an intraday minute level market reaction profile database and
find distinct responses in returns, volatility and trading volumes due to news arrivals.
We extract abnormality in minute returns, volatility and volume around the event for
the whole Russell 1000 universe.
By ranking our market reactions according to three dimensions: post-event return,
volatility and event frequency, we exhibit how big data deep taxonomy cumulated to
its large premium sources scope allows us to characterize finely the market event
reaction and thus build accordingly the most profitable customized trading strategy.
By using analytics aggregated over all its sources, we show how computed event
relevance, novelty and sentiment for company-specific news is crucial to filter out
noise and to identify significant effects and that sentiment indicators have
predictability for future price trends.
3. Quantitative Research
P a g e | 3
1. Introduction
Previous research has demonstrated (see [2]) that news signal tend to be incorporated in prices
quickly, making it advantageous to trade news intraday.
We focus here in this paper on specific intraday market reaction to a equity-specific news flow:
we follow Hautschand Gross-Klussmann(see[1]) methodology to build intraday abnormal return
and volatility at the minute level.
The paper is organized as follows:
Section 2 describes the different data sources that we use. Section 3 will detail the overall
methodology used to compute our abnormal return, volatility and volume profiles. Section 4 will
highlight how to use analytics version 4.0 taxonomy and analytics to filter the most relevant
signals and get to the most profitable event driven strategy.
An appendix will detail some of the numerous results shown here to avoid overcrowding our
main paper.
2. Data Description
In this section, we provide an overview of the datasets that we use in our research.
We describe here Big Data’s event taxonomy, which we useto define our events and its analytical
metadata which proves to be of great value to ascertain the quality of a profile.
2.1 Big Data Analytics
News event are hard to detect and collect into a unified data base and analyze in a systematic
fashion. does provide you with real time structured content fetched from premium quality
financial news sources.
We build our event trading profile library using Big Data Analytics taxonomy, event detection
and entity detection.
4. Quantitative Research
P a g e | 4
big data analytics possess all the required characteristics to build our market event reaction
database:
big data taxonomy:
automated event detection and assignment to its hierarchical taxonomy allows you to dive to
the most granular level and detect the slightest market moving event for a company. The fine
granularity of company specific event taxonomy will prove fundamental to build the most
profitable calibrated event driven trading strategy.
We here want to emphasize that Big Data analytics nearly triples the number of market-moving
events we detect (up to 6753 categories), capturing most of the nuances in language towards a
tradable company specific news. This feature will prove mostly relevant to finely assess market
reaction and make the most comprehensive event study.
entities:
news sources cover spans more than 41500 companies, 138500 places, 160 currencies and 85
commodities starting from 2000. We will focus our event studies on Russell 1000 universe for its
high liquidity.
Historical data:
Up to 16 year of millisecond time-stamped data available for market reaction profile
construction. To evaluate our profiles, we will consider data spanning a period of 10 years
covering 2007 through 2016. This historical availability is a key feature to help you build and
backtest event driven strategy.
real time feed:
All news article are assessed within milliseconds of receipt and the resulting data is immediately
pushed to the user, which is an essential component of intraday news trading: processing
velocity. We are collocated with our news feed provider and electronics markets thus
guaranteeing to process and deliver the news analytics to amachine readable structured content
within 200-250 milliseconds form the moment we receive the news
As we see in many profiles, a significant jump happens at the very event minute. The sooner the
reaction the greater the return you might capture: being timely is the essence of a good event-
driven trading strategy.
analytics:
applies entity and event specific relevance, novelty and sentiment scores to all events using
proprietary techniques. This paper will show the importance of those analytics to assess the
market reaction: we will see how analytics like news novelty or the more novel a news is, the
more the market will move. The more relevant the event is, the more the market will move. The
higher our sentiment is, the more the market will move.
5. Quantitative Research
P a g e | 5
premium sources:
And last but not least, the power of analytics, velocity, and refined taxonomy would be nothing
without broad scope of high quality financial news provider. Each of those sources focus on
certain specific and brings its expertise which when aggregated through brings the great quality
our data has always shown.
We will split up our sources study in two different specific types: Dow Jones news, Premium pack
and news content.
We will show that Premium pack (containing Benzinga,The Fly, MT_Newswire, FX_Street_News,
Alliance News, Ticker report and SleekMoney) brings great value through their specific expertise,
especially their exclusive scoop to our M&A, analyst ratings and earnings groups. This result will
be highlighted later when ranking our profiles per sources. We will see that those sources add
real value for analyst ratings and merger and acquisition.
2.2Trading volume and Pricing Data
As we focus on intraday trading patterns, we use minute data provided by Algoseek for our
Russell 1000 universe. Those minutes contain standard Open-High-Low-Close prices and trading
volumes. We derive from them minutes return, volatility and volume.
3. Intraday event minute study methodology
3.1 Abnormal returns definition
Abnormal returns are the crucial measure to assess the impact of an event. The general idea of
this measure is to isolate the effect of the event from other general market movements. The
abnormal return is defined as the difference of the realized return and the expected return given
the absence of the event.
The expected return is usually called "normal return" in academic literature and is by definition
unconditional on the event.
This normal return is to be modelled and conditioned on a separate information set, whose
definition might vary.
The definition of this past pre-event asset returns set leads to various models for the normal
return.
6. Quantitative Research
P a g e | 6
3.2 Abnormal volume and volatility
One other very important aspects of intraday high frequency trading (others than to which extent
high-frequency movements affect returns) are volatility, liquidity, trading sizes, imbalances and
spread. We will here focus mainly on volatility (as a proxy for liquidity, size and spread)
and trading volume at the minute level.
We define abnormal volume and volatility as :
where
defines the minute volatility with b being the number of minutes in an aggregated bucket, d is
the number of days in the estimation period and H,L,C refer to high, low and close price of the
specific minutes inside the bucket.
Volatility and volume are crucial factors to assess that the market reacts to news independently
of any direction and to get a sense of the scalability of any given strategy.
All volatility and liquidity variables exhibit pronounced intraday trading patterns. To capture the
seasonality and to account for an "abnormality" measure, we define our abnormality as the ratio
of the bucket measure divided by the averaged same quantity over the estimation period for all
the same minutes of the day.
3.2 Normal return model
A substantial feature of an event study is the choice of the appropriate normal return model.
All models contain parameters that need to be estimated, even the simpler constant mean return
model.
The time period over which those parameters are estimated is commonly denoted as the
estimation window.
Since the normal return is the expected return in absence of the event, overlapping event and
estimation windows should be avoided.
7. Quantitative Research
P a g e | 7
Otherwise normal return model parameters are estimated from returns affected by the event.
We will here adopt the most common approach by restricting the estimation window (T1) to the
time period prior to the event window (T2).
FIGURE 2: Estimation and event window definition. L1 i s a 6 months, L2 +/- mi nutes around the
event.
Source: , November 2016
T1 will span over 3 months before the event and T2 size will be 180 minutes spread around the
event and we will restrict ourselves to 5 minutes OHLC event bucket data and all quantity will be
computed by bunch of 5 minutes bucket.
Many market models exist only for daily event studies (CAPM, multifactor models ...). Those
models complexity is based on the factors they incorporate in addition to standard betas (market
factor) like for instance more advanced Fama-French factors. Those factors do not translate easily
to intraday high-frequency model as they are built on a daily frequency based on fundamental
data.
We here follow the methodology from Gross-Klussmann and Hautsch in [1]. We assume the
following market model for normal returns:
Where t denotes the underlying 5 minutes bucket, Rmt is the market return, whose definition is
previously exposed for the Russell 1000 universe and Rit is the bucket return for stock I and
minute bucket t. To capture return dynamics on high frequencies, we also include lagged returns
from the previous bucket.
8. Quantitative Research
P a g e | 8
3.3 Russell 1000 minute level market index
We construct our own Russell 1000 market index at the minute level by aggregating a log
capitalization weighted mean over allthe Russell1000constituents minutes returns for alltraded
minutes of the day.
The log return of our market index is computed as:
We end up with intraday minute values for our R1000 reference index, which will the basis to
compute our beta market factor and compute our abnormal returns.
3.3 Model parameters calibration
Our normal return model parameters are estimated based on a complete minute-level return
time series without including the event windows for each R1000 stock.
The size of the estimation windows is chosen to be three months. The longer the estimation the
more stable our betas and gammas will be.
We restrict ourselves to 3 months mainly due to computational issues and simply regress the
times series to compute our estimated alpha and beta for each of our stocks, to which we will
append a ^. Let's note here that we tested many different market models (constant market
model, daily betas market model) and observe always the same abnormality patterns.
3.4 Return profile
Our model parameters being calibrated, we compute the abnormal returns:
Cumulating those abnormal returns across time yields the cumulative abnormal return:
This measure is the one we have chosen to assess our abnormal return in the event window.
We will show the cross sectional average cumulated abnormal return around event news minute
with 180 minutes padding bucketed by 5 minutes.
9. Quantitative Research
P a g e | 9
For cumulative abnormal returns the cross-sectional average (CAAR) is computed as:
We will center our CAAR around the event windows and make it null for the very bucket
containing the timestamp of captured event, so that the potential returns we could make if
entering the market in the minute after the news will be displayed past zero (t>0).
Let's just mention here some other measures we tested: the buy-and-hold abnormal return
(BHAR), which is defined as the difference between the realized buy-and-hold return and the
normal buy-and-hold return
3.5 Statistical significance
In our search for profitable trading profiles, we will use analytics to its full extent to filter per
source, news novelty, entity relevance, and so on, but as we filter our events, the observations
count thus lessens and to properly discriminate among those distinct cumulative trading profiles,
one must devise a proper statistical significance measure to ponder the potential average
compounded return possibly tradable.
Many of those statistical tests have been extensively used and detailed in academic literature.
We have tested most of them and retained only the most robust one to rank our profiles: the
generalized Corrado rank test. We here follow once again [2] path.
We use the generalized cumulative Corrado test centered on the event: the whole methodology
being detailed in Appendix A. This is the best way to assess statistically the event impact over
minute’s returns both after and before event timestamp. We will see that the behavior of those
tests really depends on the news soft or hard type. The more unexpected the news is, the bigger
the statistical significance post event is. The more anticipated the news is for hard scheduled
news, the bigger the pre event statistical significance is.
10. Quantitative Research
P a g e | 10
4. Results
Having described our methodology, in this section, we evaluate the quality of our profiles. We
begin by detailing the overall average behavior at most granular level.. expanded taxonomy and
large scope of sources makes it the perfect tool to build a detailed and statistically significant
profile database.
Let’s here once again emphasize the scope of the study: we average overall big data company
specific categories (up to 6753) over the last ten years. For each category, we build profiles for a
Cartesian grid of analytics: novelty, event relevance, similarity gap and source type. We end up
here with more than 100 000 market reaction profiles. The average profile is aimed at giving us
some raw overall information.
4.1 Average profile behavior
We here show the average market reaction behavior. To avoid overfitting, we here average only
for alltaxonomy events with the same filtering methodology: similarity gap greater than one day,
high entity and event relevance. We compute the averageprofile overall sources. We here apply
no specific pondering on the profiles according to their frequency. We will see in the next
paragraph a way to filter category profiles according to different metrics.
Average abnormal returns, volatility and return at the category level:
FIGURE 4: Average cumulative abnormal returns across all category profiles. Thi s average profile
i s computed over al l sources fi l teri ng for si mi l ari ty gap greater than one day and hi gh event/enti ty
rel evance. Negati ve senti ment categori es have been reversed. The event wi ndow spans between -
180/180 mi nutes around the event.
11. Quantitative Research
P a g e | 11
FIGURE 5: Average abnormal volatility across all category profiles. Thi s average profi le is
computed over al l sources fi l teri ng for si mi l ari ty gap greater than one day and hi gh event/enti ty
rel evance
FIGURE 6: Average abnormal volume across all category profiles. Thi s average profi l e i s computed
over al l sources fi l tering for si mi l ari ty gap greater than one day and hi gh event/enti ty rel evance
12. Quantitative Research
P a g e | 12
4.2 sentiment
analytics provides each flagged event with a sentiment score, whose sign gives the direction of
the prediction and the absolute value the magnitude of the news. We use here only the
sentiment direction by splitting our incoming news in a positive or negative bucket.
FIGURE 7: Average abnormal return split by positive versus negative sentiment. Thi s average
profi l e i s computed over al l sources fi l teri ng for si mi l arity gap greater than one day and hi gh
event/enti ty rel evance
Let’s notice here that we found back a finance market well known stylized fact: financial market
tends to react in a stronger way to negative news, showing an asymmetry to the negative side.
13. Quantitative Research
P a g e | 13
4.3 Statistical significance
FIGURE 7: Average Corrado statistical profile across all categories. Thi s average profi le is
computed over al l sources fi l teri ng for si mi l ari ty gap greater than one day and hi gh event/enti ty
rel evance
We here show the average generalized Corrado rank test across all category profiles with the
same common filtering as before to avoid overfitting. The generalized Corrado test is cumulated
from both sides of the event.
The Corrado test clearly argues for an event happening and that analytics clearly flags a novel
market moving event at the right time.
Similar results are found at the group level. It exhibits the same patterns for returns, volatility
and volume, but slightly less emphasized. As groups are more frequent, the signal tends to be
less strong, with a lower expected cumulated abnormal return and surge in abnormal volatility.
Using full taxonomy granularity up to the category is the best way to create the most profitable
event driven strategies.Nevertheless trading at the group level can stillbe used for very frequent
statistical arbitrage. We display the results for the group level in the appendix.
14. Quantitative Research
P a g e | 14
4.3 Investigating our profiles
4.3.2 Ranking profiles
Ranking a profile is an arduous tasks. First the value of a profile is only to be asserted according
to the trading strategy you want to apply in the aftermath of the event. We here devise three
ranking metrics to ascertain the quality of a profile. Those metrics are chosen to fit to different
type of strategies.
ABSOLUTE RETURN :
“Absolute return” just favors absolute statistically confident returns: the best ranking
categories for that metric are the categories you must focus on to get the most profitable
event driven strategies.
FREQUENCY RETURN :
“Frequency return” ponders the confident absolute return with the frequency of the
category. You here get the perfect categories for statistical arbitrage, which show both
confident return and frequency.
VOLATILTY :
“Volatility” favors only post event volatility without considering return. This metrics is
devised to deal with category with a more sophisticated reaction (reversal behavior,
uncertainty). This metric highlights categories where you should rather device volatility
based strategy to benefit from the event.
15. Quantitative Research
P a g e | 15
For a deeper investigation of our average market reaction profile, we will now average our
profiles only among the first decile of our categories sorted according to the three different
methodologies.
We know that this opens up for overfitting, but the purpose is to highlight interesting profile
types. Future research will try to focus on in-sample vs. out-sample return predictions.
To avoid overfitting we will use the same common filtering to all categories as previously done
for the average profile with a similarity gap greater that one and a high entity and event
relevance.
We will keep consistently with red color for the volatility type, green for the pure confident
return and blue for both return and frequency.
We will also investigate source effect and split our results for two different sources types: Dow
Jones, Premium pack sources, which are the most prone to intraday reactions.
Web content sources being the results of frequent crawling, market reaction are less point-in-
time sensitive. Nevertheless Web content is still a very good source to catch the trend of
abnormal return for a stock.
We will see here that Dow Jones wire and premium pack both are sources generating a strong
market reaction in abnormal return and volatility surge.
The premium pack even seemingly slightly over performs in absolute returns metrics.
16. Quantitative Research
P a g e | 16
Dow Jones news feed:
FIGURE 7: Average abnormal return across all category profiles for Dow Jones source average
for across the first 100 ranked categories according to three metrics. RED favors volatility, BLUE
frequent return, and GREEN absolute return. Thi s average profi l e i s computed fi l teri ng for
si mi l ari ty gap greater than one day and hi gh event/enti ty rel evance
FIGURE 8: Average abnormal volatility across all category profiles for Dow Jones wire average
for across the first 100 ranked categories according to three metrics. RED favors volatility, BLUE
frequent return, and GREEN absolute return. Thi s average profi l e i s computed fi l teri ng for
si mi l ari ty gap greater than one day and hi gh event/enti ty rel evance
17. Quantitative Research
P a g e | 17
premium sources:
FIGURE 9: Average abnormal return across all category profiles for PREMIUM PACK sources
averaged across the first 100 ranked categories according to three metrics. RED favors volatility,
BLUE frequent return, and GREEN absolute return. Thi s average profi l e i s computed fi l teri ng for
si mi l ari ty gap greater than one day and hi gh event/enti ty rel evance
FIGURE 10: Average abnormal volatility across all category profiles for PREMIUM PACK sources
averaged across the first 100 ranked categories according to three metrics. R ED favors volatility,
BLUE frequent return, and GREEN absolute return. Thi s average profi l e i s computed fi l teri ng for
si mi l ari ty gap greater than one day and hi gh event/enti ty rel evance
18. Quantitative Research
P a g e | 18
4.3 Using analytics to refine our profiles
We will now investigate how analytics can help you filter out sharper market response.
We will here investigate mainly two very important dimensions: novelty metrics to assess how
novel a news is and event relevance to assess the strength of an event.
analytics 4.0 has significantly improved both analytics.
computes a similarity metrics for a specific event for an entity across all its sources. The 4.0
analytics version has increased the look back window from 100 to 365 days.
The following results expose results for Dow Jones and Premium pack type showcasing how the
similarity gap filtering affects the profile.
4.3.1 novelty assessment
Dow Jones news feed:
FIGURE 13: Similarity gap filtering effect on abnormal return for Dow Jones wire average profile.
Thi s average profi l e i s computed fi l teri ng for hi gh event/enti ty rel evance
19. Quantitative Research
P a g e | 19
FIGURE 14: Similarity gap filtering effect on abnormal volatility for Dow Jones wire average
profile. Thi s average profi l e i s computed fi l teri ng for hi gh event/enti ty rel evance
PREMIUM PACK:
FIGURE 15: Similarity gap filtering effect on abnormal return for PREMIUM PACK average profile.
Thi s average profi l e i s computed fi l teri ng for hi gh event/enti ty rel evance
20. Quantitative Research
P a g e | 20
FIGURE 16: Similarity gap filtering effect on abnormal volatility for Dow Jones wire average
profile. Thi s average profi l e i s computed fi l teri ng for hi gh event/enti ty rel evance
We here clearly see that the more novel a news is, the more the market will move in term of
abnormal return and volatility surge. It is notable to see here that even for news without novelty
a slight market reaction occurs. This is due to the fact that repeated news tend to still deeper
instill their message into stocks prices at a time of globalization where investors do not live in the
same time zone.
4.3.2 event relevance
developed a news score that indicates how relevant an event is within a story. The scores
fluctuate 0-100. We bucket those values as high (>=90), middle (>=70 and <90) and low (<70).
Our high confidence means that the event for the specific entity has been matched in the
headline. The following results expose results for Dow Jones and Premium pack type showcasing
how the event relevance filtering affects the profile.
21. Quantitative Research
P a g e | 21
Dow Jones wire news feed:
FIGURE 17: Event relevance effect on abnormal return for Dow Jones average profile. Thi s average
profi l e i s computed fi l teri ng for si mi l ari ty gap greater than one day
FIGURE 18: Event relevance effect on abnormal volatility for Dow Jones average profile. This
average profi l e i s computed fi l teri ng for si mi l ari ty gap greater than one day
22. Quantitative Research
P a g e | 22
PREMIUM PACK:
FIGURE 19: Event relevance effect on abnormal return for PREMIUM PACK average profile. This
average profi l e i s computed fi l teri ng for si mi l ari ty gap greater than one day
FIGURE 20: Event relevance effect on abnormal volatility for PREMIUM PACK average profile. This
average profi l e i s computed fi l teri ng for si mi l ari ty gap greater than one day
23. Quantitative Research
P a g e | 23
It is clear that both novelty measures and event relevance matter in our profile selection.
The lower the event relevance, the less steep is the abnormal return jump and volatility surge
are.
Nevertheless it is important to notice here that those news still matter for the specific company
in the sense that the abnormal return is not a white noise but clearly trends in the direction of
the news sentiment. So those profiles are more momentum driven profiles, from which you can
stillextract valuefrom intraday trading. It emphasizes the importance of Big Dataanalytics where
all observation possess value. Even low relevance/less novel entries still possess value for
intraday trading. The market profiles account then more as aquantification of the trend following
momentum still providing great value for intraday trading.
4.4 Ranking groups and category
The last step of our investigation is to dig always deeper in taxonomy and investigate now all
categories in a comparative table.
Now that we have established how big data analytics filtering mattered, we will present with a
comprehensive ranking for all groups and entities according to the FREQUENCY metric and the
VOLATILITY one. We restrict ourselves to those two metrics and the first 30, but more
comprehensive results will be displayed in the appendix.
Note here the presence of our PREMIUM pack sources which provide their value and expertise
for analyst ratings and merger and acquisitions.
Notice also the presence of our web content for more soft economic categories which
demonstrate the value of the web content for those topics.
28. Quantitative Research
P a g e | 28
4.4 Displayingthefirst three best groupsand their2best categories foreach
FIGURE 21: Abnormal returns, group Analyst Ratings, sources Premium Pack, ranking 5 stars
FREQUENCY
FIGURE 22: Abnormal volatility, group Analyst Ratings, sources Premium Pack, ranking 5 stars for
FREQUENCY
29. Quantitative Research
P a g e | 29
FIGURE 23: Abnormal returns, group Credit Ratings, sources DJPR, ranking 5 stars for FREQUENC Y
FIGURE 24: Abnormal returns, group Credit Ratings, sources DJPR, ranking 5 stars for FREQUENC Y
30. Quantitative Research
P a g e | 30
FIGURE 25: Abnormal returns, group Credit Ratings, sources DJPR, ranking 5 stars for FREQUENC Y
FIGURE 26: Abnormal volatility, group Insider Trading, sources DJP R, ranking 5 stars for
FREQUENCY
41. Quantitative Research
P a g e | 41
5. Conclusion
Wehave here shown that Big Data analytics is the mostcompelling tool to build a market reaction
database.
We have shown how its analytics helps you filter out noise and get to the stronger intraday signal.
This study is a big data study as our financial spans the R1000 universe for the past ten years at
the minute level with the requirement of 3 months historic data to compute our abnormal return
and volatility.
We have done that in a very general way for all categories generally and shown a comprehensive
ranking.
The next step would be to focus on single specific categories and use most advanced analytics
to improve them: big data release provides us with new dimensions to do exactly that: Fact level
tagging, event temporal assessment and category specific analytics (like our earnings type) will
even more refine our profiles to give you a statistical edge.
Then when market reaction is calibrated to the finest level, the next will be to build an event trading
strategy based on those response calibration. This will be the next step of our research.
42. Quantitative Research
P a g e | 42
6. References
[1] Axel Gross-Klussmann and Nikolaus Hautsch (2011), “When machines read the news: Using
automated text analytics to quantify high frequency news-implied market reactions”, Journal of
Empirical Finance
[2] Quantamentals (September 2012), “Quantifying events”
[3] Peter Hafez and Jose A. Guerrero-Colón (March 2016), “Earnings Sentiment Consistently
Outperforms Consensus”, Quantitative Research
[4] Eugene F.Fama, Lawrence Fisher, Michael C. Jensen, Richard Roll, “The Adjustment of Stock
Prices to New Information”, International Economic Review, Vol 10
[5] Nikolaus Hautsch (2008), “Capturing common components in high frequency time series: a
multivariate stochastic multiplicative error model”, J. Econ.Dyn. Control 32
[6] Event study metrics methodology book, http://eventstudymetrics.com/
43. Quantitative Research
P a g e | 43
Appendix A: Generalized Cumulative Corrado Test
Corrado rank test:
In our search for profitable trading profiles, we will use analytics to its full extent to filter per
source, news novelty, entity relevance, and so on, but as we filter our events, the observations
count lessens and to properly discriminate among those distinct cumulative trading profiles, one
must devise a proper statistical significance measure to ponder the potential average
compounded return possibly tradable.
Many of those statistical tests have been extensively used and detailed in academic literature.
We have tested most of them and retained only the most robust one to rank our profiles: the
generalized Corrado rank test.
The Corrado rank test statistic is based on the rank of the abnormal returns standardized by their
standard deviation over both the estimation window and event window combined T=T_1+T_2.
Generalized Corrado rank test:
In order to account for the possible event induced volatility (following Boehmer, Mucumeci and
Poulsen (1991)), the generalized Corrado test restandardize the SARs with the cross sectional
standard deviation of the SAR defining the generalized standardized abnormal return.
Where
Is the standard deviation of the event minute standardized abnormal return (the over line
meaning average cross sectional standardized abnormal return on the event minute).
The Corrado and Zivney (1992) test statistic (CZ) is then:
44. Quantitative Research
P a g e | 44
Where
Are the cross-sectional average over the nt number of valid returns on bucket t.
It is notable (and computationally intensive!) that the generalized CZ test uses both the
estimation period and the event period observations.
Cumulative generalized Corrado rank test centered on the event:
In order to test mean return on event windows longer than one bucket, which is our case with
our 180 minutes padding, (following Cowan and Campbell and Wasley (2003)) suggest simply to
aggregate the ranks over the window (CUM RANK) :
Where
Is just the cumulated sum of each minute bucket Corrado rank.
We use this methodology centered on the event: the cumulative rank generalized Corrado test
in the event widow is computed cumulatively around the event based on the distance to the
precise minute bucket when the event happened.
45. Quantitative Research
P a g e | 45
Terms of Use
This White Paper is not intended for trading purposes. The White Paper is not appropriate for the
purposesof makinga decisiontocarry outa transactionor trade.Nor doesit provide anyformof advice
(investment, tax, legal) amounting to investment advice, or make any recommendations regarding
particular financial instruments, investments or products. may discontinue or change the White Paper
content at any time, without notice. does not guarantee or warrant the accuracy, completeness or
timeliness of the White Paper
You may not post any content from this White Paper to forums, websites, newsgroups, mail lists,
electronicbulletinboards,orotherservices,withoutthe priorwrittenconsentof .To requestconsentfor
this and other matters, you may contact at research@.com.
THE WHITE PAPER ISPROVIDED“AS IS”, WITHOUT ANY WARRANTIES. ANDITS AFFILIATES,AGENTSAND
LICENSORS CANNOT AND DO NOT WARRANT THE ACCURACY, COMPLETENESS, CURRENTNESS,
TIMELINESS,NONINFRINGEMENT,TITLE,MERCHANTABILITYORFITNESSFORA PARTICULARPURPOSEOF
THE WHITE PAPER,AND HEREBY DISCLAIMSANYSUCHEXPRESSORIMPLIEDWARRANTIES.NEITHER NOR
ANYOF ITS AFFILIATES,AGENTSORLICENSORSSHALL BE LIABLE TO YOU OR ANYONEELSE FOR ANYLOSS
OR INJURY, OTHER THAN DEATH OR PERSONAL INJURY RESULTING DIRECTLY FROM USE OF THE WHITE
PAPER, CAUSED IN WHOLE OR PART BY ITS NEGLIGENCE OR CONTINGENCIES BEYOND ITS CONTROL IN
PROCURING, COMPILING, INTERPRETING, REPORTING OR DELIVERING THE WHITE PAPER. IN NO EVENT
WILL , ITS AFFILIATES, AGENTS OR LICENSORS BE LIABLE TO YOU OR ANYONE ELSE FOR ANY DECISION
MADE ORACTION TAKEN BYYOU IN RELIANCEON SUCH WHITE PAPER. ANDITSAFFILIATES,AGENTSAND
LICENSORS SHALL NOT BE LIABLE TO YOU OR ANYONE ELSE FOR ANY DAMAGES (INCLUDING, WITHOUT
LIMITATION, CONSEQUENTIAL, SPECIAL, INCIDENTAL, INDIRECT, OR SIMILAR DAMAGES), OTHER THAN
DIRECT DAMAGES, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. IN NO EVENT SHALL THE
LIABILITY OF , ITS AFFILIATES, AGENTS AND LICENSORS ARISING OUT OF ANY CLAIM RELATED TO THIS
AGREEMENT EXCEED THE AGGREGATE AMOUNTPAIDBY YOU FOR THE WHITE PAPER.JURISDICTIONSDO
NOTALLOW THE EXCLUSION ORLIMITATION OFLIABILITYFORDAMAGES ORTHE EXCLUSION OFCERTAIN
TYPES OF WARRANTIES, PARTS OR ALL OF THE ABOVE LIMITATION MAY NOT APPLY TO YOU.
These Termsof Use,your rightsand obligations,andall actionscontemplatedbythese Termsof Use will
be governedbythe lawsof NewYork, NY, USA and You and agree to submitto the exclusive jurisdiction
of the NewYorkCourts.If anyprovisionintheseTermsof Use isinvalidorunenforceableunderapplicable
law, the remaining provisions will continue in full force and effect, and the invalid or unenforceable
provision will be deemed superseded by a valid, enforceable provision that most closely matches the
intent of the original provision.