We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping...

We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping...

This research paper aim to examine the profitability of various kinds of oscillator used in technical analysis on market index of NSE (National Stock Exchange) S&P CNX NIFTY 50 during 2004-2014. We have selected the most commonly used three oscillators i.e., Stochastic oscillator, RSI Oscillator...

The existing replication policies at top finance journals are far weaker than the policies at top economics journals. This paper explores both the costs and benefits of having a stronger replication policy in the context of my failed 2010 initiative to develop a unified policy across all top...

An analysis of about 300000 earnings forecasts, created by 18000 individual forecasters for earnings of over 300 S&P listed firms, shows that these forecasts are predictable to a large extent using a statistical model that includes publicly available information. When we focus on the...

The use of fundamentalist traders in the stock market models is problematic since fundamental values in the real world are unknown. Yet, in the literature to date, fundamentalists are often required to replicate key stylized facts. The authors present an agent-based model of the stock market in...

This article aims to explore the main areas of research, development trends and provide a systematic overview of publications in the field of artificial intelligence in financial markets. The bibliometric tool VOSViewer is used in this paper. We analyzed 353 articles and contributions obtained...

We conduct a fundamental analysis of detailed financial information to predict earnings. Since 2012, all U.S. public companies must tag quantitative amounts in financial statements and footnotes of their 10-K reports using the eXtensible Business Reporting Language (XBRL). Leveraging machine...

Quantitative research analysts (Quants) produce in-depth quantitative and econometric modeling of market anomalies to assist sell-side analysts and institutional clients with stock selection strategies. Quants are associated with more efficient analyst forecasting behavior on anomaly predictors...

We study whether “big data” can help market participants form more accurate beliefs. We look at analysts and record when they explicitly reference the use of big data in their written reports. We find that analysts frequently draw from big data, particularly when receiving more timely...