Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market

We report successful results from using deep learning neural networks (DLNNs) to learn, purely by observation, the behavior of profitable traders in an electronic market closely modelled on the limit-order-book (LOB) market mechanisms that are commonly found in the real-world global financial markets for equities (stocks & shares), currencies, bonds, commodities, and derivatives. Successful real human traders, and advanced automated algorithmic trading systems, learn from experience and adapt over time as market conditions change; our DLNN learns to copy this adaptive trading behavior. A novel aspect of our work is that we do not involve the conventional approach of attempting to predict time-series of prices of tradeable securities. Instead, we collect large volumes of training data by observing only the quotes issued by a successful sales-trader in the market, details of the orders that trader is executing, and the data available on the LOB (as would usually be provided by a centralized exchange) over the period that the trader is active. In this paper we demonstrate that suitably configured DLNNs can learn to replicate the trading behavior of a successful adaptive automated trader, an algorithmic system previously demonstrated to outperform human traders. We also demonstrate that DLNNs can learn to perform better (i.e., more profitably) than the trader that provided the training data. We believe that this is the first ever demonstration that DLNNs can successfully replicate a human-like, or super-human, adaptive trader operating in a realistic emulation of a real-world financial market. Our results can be considered as proof-of-concept that a DLNN could, in principle, observe the actions of a human trader in a real financial market and over time learn to trade equally as well as that human trader, and possibly better.Comment: 8 pages, 4 figures. To be presented at IEEE Symposium on Computational Intelligence in Financial Engineering (CIFEr), Bengaluru; Nov 18-21, 201

A Review and Bibliography of Early Warning Models

This note is intended to share some observations regarding a non-exhaustive collection of the early warning literature from 1971 to 2011. Evolution of the interest in early warning models, methodological spectrum of studies and coverage of economic variables are briefly discussed in addition to providing a bibliography.Early warning systems, bibliometric analysis

The Highest Price Ever: The Great NYSE Seat Sale of 1928–1929 and Capacity Constraints

During the 1920s the New York Stock Exchange's position as the dominant American exchange was eroding. Costs to customers, measured as bid-ask spreads, spiked when surging inflows of orders collided with the constraint created by a fixed number of brokers. The NYSE's management proposed and the membership approved a 25 percent increase in the number of seats by issuing a quarter-seat dividend to all members. An event study reveals that the aggregate value of the NYSE rose in anticipation of improved competitiveness. These expectations were justified as bid-ask spreads became less sensitive to peak volume days

Do Jumps Matter? Forecasting Multivariate Realized Volatility allowing for Common Jumps

Realized volatility of stock returns is often decomposed into two distinct components that are attributed to continuous price variation and jumps. This paper proposes a tobit multivariate factor model for the jumps coupled with a standard multivariate factor model for the continuous sample path to jointly forecast volatility in three Chinese Mainland stocks. Out of sample forecast analysis shows that separate multivariate factor models for the two volatility processes outperform a single multivariate factor model of realized volatility, and that a single multivariate factor model of realized volatility outperforms univariate models.Realized Volatility, Bipower Variation, Jumps, Common Factors, Forecasting

Style anomalies on the London Stock Exchange : an analysis of univariate, multivariate and timing strategies

According to Dimson (1998), modem financial theory is founded on the assumption that markets are highly efficient. The presence of anomalous stock market behaviour has therefore attracted a great amount of research internationally. This thesis investigates the presence and exploitability of style anomalies on the London Stock Exchange (LSE) and is divided into three main branches of research

THE EFFECT OF GOOGLE TREND AS DETERMINANT OF RETURN AND LIQUIDITY IN INDONESIA STOCK EXCHANGE

The impressive progress of information technology has substantially impacted economic development. Given this condition, the diffusion of information technology is related to the improvement of activities in the capital market, in which asymmetric information between investors can diminished to the lowest level. Thereby, we considered that information retrieval over the internet contributes to return and liquidity. We performed Google Trend (GT) as the surrogated indicator in attenuating the asymmetric information in Indonesia Stock Exchange. By utilizing 5976 observation data from 83 cross-sectional companies and 72 monthly time series ranging from January 2007 to December 2012, we noted that the information retrieval over the Internet has negative (p < 0.05) contribution to return (RET). On the other hand, we confirmed that the information retrieval over the internet (GT) is positively (p < 0.01) related to liquidity which is surrogated by trading volume (TV)

Were Japanese Stock Prices Too High?

The difference between reported price-earnings ratios in the United States and Japan is not as puzzling as it appears at first glance. Nearly half the disparity is caused by differences in accounting practices with respect to consolidation of earnings from subsidiaries and depreciation of fixed assets. If Japanese firms used U.S. accounting rules, we estimate that the P/E ratio for the Tokyo Stock Exchange would have been 32.1, not the reported 54.3, at the end of 1988. Accounting differences are unable, however, to explain the sharp rise in the Japanese stock market during the mid-1980s. Changes in required returns on equities, or in investor expectations of future growth for Japanese firms, must be invoked to explain this phenomenon. Real interest rates declined during the period of rapid price increase, but there is little evidence that growth expectations became more optimistic. The real interest rate changes do not, however, appear large enough to fully account for the change in stock prices.