Research
In 2010, Preis headed a research team which provided evidence that search engine query data and stock market fluctuations are correlated. [6] [7] [8] [9] [10] The team discovered a link between the number of Internet searches for company names and transaction volumes of the corresponding stocks on a weekly time scale. [11] In a TEDx talk, [12] Preis highlights the opportunities offered by studies of citizens' online behaviour to gain insights into socio and economic decision making.
In 2012, Preis used Google Trends data to demonstrate together with his colleagues Suzy Moat, H. Eugene Stanley and Steven R. Bishop that Internet users from countries with a higher per capita gross domestic product (GDP) are more likely to search for information about the future than information about the past. The findings, published in the journal Scientific Reports , suggest there may be a link between online behaviour and real-world economic indicators. [13] [14] [15] [16] Preis and colleagues examined Google search queries made by Internet users in 45 different countries in 2010 and calculated the ratio of the volume of searches for the coming year (2011) to the volume of searches for the previous year (2009), which they call the Future Orientation Index. A comparison of the Future Orientation Index to the per capita GDP of each country revealed a strong tendency for countries in which Google users enquire more about the future to exhibit a higher GDP. Preis and colleagues conclude from this study that a relationship potentially exists between the economic success of a country and the information-seeking behaviour of its citizens online. [13] [17] [18] [19] [20]
In 2013, Preis and his colleagues Moat and Stanley introduced a method to identify online precursors for stock market moves, using trading strategies based on search volume data provided by Google Trends. [21] Their analysis of Google search volume for 98 terms of varying financial relevance, published in Scientific Reports , [22] suggests that increases in search volume for financially relevant search terms tend to precede large losses in financial markets. [23] [24] [25] [26] [27] [28] [29] [30] Similarly, in a study also published in Scientific Reports in 2013, [31] Moat, Preis and colleagues demonstrated a link between changes in the number of views of Wikipedia articles relating to financial topics and subsequent large stock market moves. [32]
In 2015, Preis and his colleague Moat designed and delivered a massive open online course (MOOC) on big data. The course focuses on measuring and predicting human behavior. [33]
Preis is an academic editor of PLoS ONE. [34]
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.
Prediction markets, also known as betting markets, information markets, decision markets, idea futures or event derivatives, are open markets that enable the prediction of specific outcomes using financial incentives. They are exchange-traded markets established for trading bets in the outcome of various events. The market prices can indicate what the crowd thinks the probability of the event is. A typical prediction market contract is set up to trade between 0 and 100%. The most common form of a prediction market is a binary option market, which will expire at the price of 0 or 100%. Prediction markets can be thought of as belonging to the more general concept of crowdsourcing which is specially designed to aggregate information on particular topics of interest. The main purposes of prediction markets are eliciting aggregating beliefs over an unknown future outcome. Traders with different beliefs trade on contracts whose payoffs are related to the unknown future outcome and the market prices of the contracts are considered as the aggregated belief.
Eugene Francis "Gene" Fama is an American economist, best known for his empirical work on portfolio theory, asset pricing, and the efficient-market hypothesis.
Analytics is the systematic computational analysis of data or statistics. It is used for the discovery, interpretation, and communication of meaningful patterns in data. It also entails applying data patterns toward effective decision-making. It can be valuable in areas rich with recorded information; analytics relies on the simultaneous application of statistics, computer programming, and operations research to quantify performance.
Econophysics is a non-orthodox interdisciplinary research field, applying theories and methods originally developed by physicists in order to solve problems in economics, usually those including uncertainty or stochastic processes and nonlinear dynamics. Some of its application to the study of financial markets has also been termed statistical finance referring to its roots in statistical physics. Econophysics is closely related to social physics.
A market anomaly in a financial market is predictability that seems to be inconsistent with theories of asset prices. Standard theories include the capital asset pricing model and the Fama-French Three Factor Model, but a lack of agreement among academics about the proper theory leads many to refer to anomalies without a reference to a benchmark theory. Indeed, many academics simply refer to anomalies as "return predictors", avoiding the problem of defining a benchmark theory.
The random walk hypothesis is a financial theory stating that stock market prices evolve according to a random walk and thus cannot be predicted.
In web analytics and website management, a pageview or page view, abbreviated in business to PV and occasionally called page impression, is a request to load a single HTML file of an Internet site. On the World Wide Web, a page request would result from a web surfer clicking on a link on another page pointing to the page in question.
Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume. This type of trading attempts to leverage the speed and computational resources of computers relative to human traders. In the twenty-first century, algorithmic trading has been gaining traction with both retail and institutional traders. A study in 2019 showed that around 92% of trading in the Forex market was performed by trading algorithms rather than humans.
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.
VIX is the ticker symbol and the popular name for the Chicago Board Options Exchange's CBOE Volatility Index, a popular measure of the stock market's expectation of volatility based on S&P 500 index options. It is calculated and disseminated on a real-time basis by the CBOE, and is often referred to as the fear index or fear gauge.
Google Finance is a website focusing on business news and financial information hosted by Google.
Google Trends is a website by Google that analyzes the popularity of top search queries in Google Search across various regions and languages. The website uses graphs to compare the search volume of different queries over time.
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.
Robert James Shiller is an American economist, academic, and author. As of 2022, he served as a Sterling Professor of Economics at Yale University and is a fellow at the Yale School of Management's International Center for Finance. Shiller has been a research associate of the National Bureau of Economic Research (NBER) since 1980, was vice president of the American Economic Association in 2005, its president-elect for 2016, and president of the Eastern Economic Association for 2006–2007. He is also the co‑founder and chief economist of the investment management firm MacroMarkets LLC.
Seeking Alpha is a crowd-sourced content service that publishes news on financial markets. It is accessible via a website and mobile app. After a free trial period, users must pay a subscription fee to access content. Independent contributors, mostly from the buy side, write almost all of the articles published by the service and are paid based on how many subscribers access their articles. Notable contributors include Henry Blodget and Paco Ahlgren.
Wikipedia has been studied extensively. Between 2001 and 2010, researchers published at least 1,746 peer-reviewed articles about the online encyclopedia. Such studies are greatly facilitated by the fact that Wikipedia's database can be downloaded without help from the site owner.
Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity may lead to a higher false discovery rate. Though used sometimes loosely partly due to a lack of formal definition, the best interpretation is that it is a large body of information that cannot be comprehended when used in small amounts only.
Google Flu Trends (GFT) was a web service operated by Google. It provided estimates of influenza activity for more than 25 countries. By aggregating Google Search queries, it attempted to make accurate predictions about flu activity. This project was first launched in 2008 by Google.org to help predict outbreaks of flu.
The Future Orientation Index was introduced by Tobias Preis, Helen Susannah Moat, H. Eugene Stanley and Steven Bishop using Google Trends to demonstrate that Google users from countries with a higher per capita GDP are more likely to search for information about the future than information about the past. The findings suggest there may be a link between online behaviour and real-world economic indicators. The authors of the study examined Google query logs made by Google users in 45 different countries in 2010 and calculated the ratio of the volume of searches for the coming year (‘2011’) to the volume of searches for the previous year (‘2009’).
