 | A major contributor to this article appears to have a close connection with its subject.(February 2022) |
Arizona Financial Text System (AZFinText) is a textual-based quantitative financial prediction system written by Robert P. Schumaker of University of Texas at Tyler and Hsinchun Chen of the University of Arizona.
This system differs from other systems in that it uses financial text as one of its key means of predicting stock price movement. This reduces the information lag-time problem evident in many similar systems where new information must be transcribed (e.g., such as losing a costly court battle or having a product recall), before the quant can react appropriately. AZFinText overcomes these limitations by utilizing the terms used in financial news articles to predict future stock prices twenty minutes after the news article has been released. [1]
It is believed that certain article terms can move stocks more than others. Terms such as factory exploded or workers strike will have a depressing effect on stock prices whereas terms such as earnings rose will tend to increase stock prices.
When a human trading expert sees certain terms, they will react in a somewhat predictable fashion. AZFinText capitalizes on the arbitrage opportunities that exist when investment experts over and under-react to certain news stories. By analyzing breaking financial news articles and focusing on specific parts of speech, portfolio selection, term weighting and even article sentiment, the AZFinText system becomes a powerful tool and is a radically different way of looking at stock market prediction.
The foundation of AZFinText can be found in the ACM TOIS article. [2] Within this paper, the authors tested several different prediction models and linguistic textual representations. From this work, it was found that using the article terms and the price of the stock at the time the article was released was the most effective model and using proper nouns was the most effective textual representation technique. Combining the two, AZFinText netted a 2.84% trading return over the five-week study period.
AZFinText was then extended to study what combination of peer organizations help to best train the system. [3] Using the premise that IBM has more in common with Microsoft than GM, AZFinText studied the effect of varying peer-based training sets. To do this, AZFinText trained on the various levels of GICS and evaluated the results. It was found that sector-based training was most effective, netting an 8.50% trading return, outperforming Jim Cramer, Jim Jubak and DayTraders.com during the study period. AZFinText was also compared against the top 10 quantitative systems and outperformed 6 of them.
A third study investigated the role of portfolio building in a textual financial prediction system. [4] From this study, Momentum and Contrarian stock portfolios were created and tested. Using the premise that past winning stocks will continue to win and past losing stocks will continue to lose, AZFinText netted a 20.79% return during the study period. It was also noted that traders were generally overreacting to news events, creating the opportunity of abnormal returns.
A fourth study looked into using author sentiment as an added predictive variable. [5] Using the premise that an author can unwittingly influence market trades simply by the terms they use, AZFinText was tested using tone and polarity features. It was found that Contrarian activity was occurring within the market, where articles of a positive tone would decrease in price and articles of a negative tone would increase in price.
A further study investigated what article verbs have the most influence on stock price movement. [6] From this work, it was found that planted, announcing, front, smaller and crude had the highest positive impact on stock price.
AZFinText has been the topic of discussion by numerous media outlets. Some of the more notable ones include The Wall Street Journal , [7] MIT's Technology Review , [8] Dow Jones Newswire, [9] WBIR [10] in Knoxville, TN, Slashdot [11] and other media outlets.
Finance is the study and discipline of money, currency and capital assets. It is related to, but not synonymous with economics, which is the study of production, distribution, and consumption of money, assets, goods and services . Finance activities take place in financial systems at various scopes, thus the field can be roughly divided into personal, corporate, and public finance.
Fundamental analysis, in accounting and finance, is the analysis of a business's financial statements ; health; and competitors and markets. It also considers the overall state of the economy and factors including interest rates, production, earnings, employment, GDP, housing, manufacturing and management. There are two basic approaches that can be used: bottom up analysis and top down analysis. These terms are used to distinguish such analysis from other types of investment analysis, such as quantitative and technical.
A stock market, equity market, or share market is the aggregation of buyers and sellers of stocks, which represent ownership claims on businesses; these may include securities listed on a public stock exchange, as well as stock that is only traded privately, such as shares of private companies which are sold to investors through equity crowdfunding platforms. Investment is usually made with an investment strategy in mind.
In finance, technical analysis is an analysis methodology for analysing and forecasting the direction of prices through the study of past market data, primarily price and volume. Behavioral economics and quantitative analysis use many of the same tools of technical analysis, which, being an aspect of active management, stands in contradiction to much of modern portfolio theory. The efficacy of both technical and fundamental analysis is disputed by the efficient-market hypothesis, which states that stock market prices are essentially unpredictable, and research on whether technical analysis offers any benefit has produced mixed results.
The efficient-market hypothesis (EMH) is a hypothesis in financial economics that states that asset prices reflect all available information. A direct implication is that it is impossible to "beat the market" consistently on a risk-adjusted basis since market prices should only react to new information.
Day trading is a form of speculation in securities in which a trader buys and sells a financial instrument within the same trading day, so that all positions are closed before the market closes for the trading day to avoid unmanageable risks and negative price gaps between one day's close and the next day's price at the open. Traders who trade in this capacity are generally classified as speculators. Day trading contrasts with the long-term trades underlying buy-and-hold and value investing strategies. Day trading may require fast trade execution, sometimes as fast as milli-seconds in scalping, therefore direct-access day trading software is often needed.
Value investing is an investment paradigm that involves buying securities that appear underpriced by some form of fundamental analysis. The various forms of value investing derive from the investment philosophy first taught by Benjamin Graham and David Dodd at Columbia Business School in 1928, and subsequently developed in their 1934 text Security Analysis.
In finance, statistical arbitrage is a class of short-term financial trading strategies that employ mean reversion models involving broadly diversified portfolios of securities held for short periods of time. These strategies are supported by substantial mathematical, computational, and trading platforms.
Contrarian investing is an investment strategy that is characterized by purchasing and selling in contrast to the prevailing sentiment of the time.
A stock trader or equity trader or share trader, also called a stock investor, is a person or company involved in trading equity securities and attempting to profit from the purchase and sale of those securities. Stock traders may be an investor, agent, hedger, arbitrageur, speculator, or stockbroker. Such equity trading in large publicly traded companies may be through a stock exchange. Stock shares in smaller public companies may be bought and sold in over-the-counter (OTC) markets or in some instances in equity crowdfunding platforms.
Market sentiment, also known as investor attention, is the general prevailing attitude of investors as to anticipated price development in a market. This attitude is the accumulation of a variety of fundamental and technical factors, including price history, economic reports, seasonal factors, and national and world events. If investors expect upward price movement in the stock market, the sentiment is said to be bullish. On the contrary, if the market sentiment is bearish, most investors expect downward price movement. Market participants who maintain a static sentiment, regardless of market conditions, are described as permabulls and permabears respectively. Market sentiment is usually considered as a contrarian indicator: what most people expect is a good thing to bet against. Market sentiment is used because it is believed to be a good predictor of market moves, especially when it is more extreme. Very bearish sentiment is usually followed by the market going up more than normal, and vice versa. A bull market refers to a sustained period of either realized or expected price rises, whereas a bear market is used to describe when an index or stock has fallen 20% or more from a recent high for a sustained length of time.
The following outline is provided as an overview of and topical guide to finance:
Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on an exchange. The successful prediction of a stock's future price could yield significant profit. The efficient-market hypothesis suggests that stock prices reflect all currently available information and any price changes that are not based on newly revealed information thus are inherently unpredictable. Others disagree and those with this viewpoint possess myriad methods and technologies which purportedly allow them to gain future price information.
The simExchange is a web-based prediction market in which players use virtual money to buy and sell stocks and futures contracts in upcoming video game properties. The main purpose of the web site is to predict trends in the video game industry, particularly how upcoming products will sell and how they will be received by the critics. For those who do not participate in the prediction market, the web site is a database of sales forecasts and game quality forecasts that are updated in real-time. The web site also features a number of "Wisdom of the crowd"-type content collaboration and aggregation tools, including means for sharing information, articles, images, and videos about the games.
Quantitative behavioral finance is a new discipline that uses mathematical and statistical methodology to understand behavioral biases in conjunction with valuation.
A financial data vendor provides market data to financial firms, traders, and investors. The data distributed is collected from sources such as stock exchange feeds, brokers and dealer desks or regulatory filings.
Clifford Scott Asness is an American hedge fund manager and the co-founder of AQR Capital Management.
Quantitative analysis is the use of mathematical and statistical methods in finance and investment management. Those working in the field are quantitative analysts (quants). Quants tend to specialize in specific areas which may include derivative structuring or pricing, risk management, investment management and other related finance occupations. The occupation is similar to those in industrial mathematics in other industries. The process usually consists of searching vast databases for patterns, such as correlations among liquid assets or price-movement patterns.
Robert P. Schumaker is an American academic and Professor of computer science at the University of Texas at Tyler, best known for creating AZFinText, a news-aware high-frequency stock trading system. Schumaker is also known as a Sports Analytics expert for his pioneering work using Twitter tweet sentiment to predict sports outcomes and is currently active in both prescription drug interactions and covid-19 vaccine allergies. Schumaker is also the founder and Director of the Data Analytics Lab.
ESG Quant is an investment strategy, developed by Arabesque Partners, which involves quantitative equity investing while utilizing ESG information, often referred to as "non-financial" information. ESG Quant strategies are implemented within systematic trading or quantitative trading approaches that leverage a large and growing collection of commercial ESG, alternative and non-profit or academic datasets. As such, there is no human judgment or discretionary buy-sell decision making; rather, “in a pure quant model the final decision to buy or sell is made by the model” or through the “utilization of an expert system that replicates previously captured actions of real traders.”