35,681 research outputs found
Enhancing Stock Movement Prediction with Adversarial Training
This paper contributes a new machine learning solution for stock movement
prediction, which aims to predict whether the price of a stock will be up or
down in the near future. The key novelty is that we propose to employ
adversarial training to improve the generalization of a neural network
prediction model. The rationality of adversarial training here is that the
input features to stock prediction are typically based on stock price, which is
essentially a stochastic variable and continuously changed with time by nature.
As such, normal training with static price-based features (e.g. the close
price) can easily overfit the data, being insufficient to obtain reliable
models. To address this problem, we propose to add perturbations to simulate
the stochasticity of price variable, and train the model to work well under
small yet intentional perturbations. Extensive experiments on two real-world
stock data show that our method outperforms the state-of-the-art solution with
3.11% relative improvements on average w.r.t. accuracy, validating the
usefulness of adversarial training for stock prediction task.Comment: IJCAI 201
Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on Twitter
Microblogs are increasingly exploited for predicting prices and traded
volumes of stocks in financial markets. However, it has been demonstrated that
much of the content shared in microblogging platforms is created and publicized
by bots and spammers. Yet, the presence (or lack thereof) and the impact of
fake stock microblogs has never systematically been investigated before. Here,
we study 9M tweets related to stocks of the 5 main financial markets in the US.
By comparing tweets with financial data from Google Finance, we highlight
important characteristics of Twitter stock microblogs. More importantly, we
uncover a malicious practice - referred to as cashtag piggybacking -
perpetrated by coordinated groups of bots and likely aimed at promoting
low-value stocks by exploiting the popularity of high-value ones. Among the
findings of our study is that as much as 71% of the authors of suspicious
financial tweets are classified as bots by a state-of-the-art spambot detection
algorithm. Furthermore, 37% of them were suspended by Twitter a few months
after our investigation. Our results call for the adoption of spam and bot
detection techniques in all studies and applications that exploit
user-generated content for predicting the stock market
"Uncertainty, Conventional Behavior, and Economic Sociology"
This paper addresses the problem of the conceptualization of social structure and its relationship to human agency in economic sociology. The background is provided by John Maynard KeynesÕs observations on the effects of uncertainty and conventional behavior on the stock market; the analysis consists of a comparison of the social ontologies of the French Intersubjectivist School and the Economics as Social Theory Project in the light of these observations. The theoretical argument is followed by concrete examples drawn from a prominent recent study of the stock market boom of the 1990s.
Uncertainty, Conventional Behavior, and Economic Sociology
This paper addresses the problem of the conceptualization of social structure and its relationship to human agency in economic sociology. The background is provided by John Maynard Keynes's observations on the effects of uncertainty and conventional behavior on the stock market; the analysis consists of a comparison of the social ontologies of the French Intersubjectivist School and the Economics as Social Theory Project in the light of these observations. The theoretical argument is followed by concrete examples drawn from a prominent recent study of the stock market boom of the 1990s.
Can Google Trends search queries contribute to risk diversification?
Portfolio diversification and active risk management are essential parts of
financial analysis which became even more crucial (and questioned) during and
after the years of the Global Financial Crisis. We propose a novel approach to
portfolio diversification using the information of searched items on Google
Trends. The diversification is based on an idea that popularity of a stock
measured by search queries is correlated with the stock riskiness. We penalize
the popular stocks by assigning them lower portfolio weights and we bring
forward the less popular, or peripheral, stocks to decrease the total riskiness
of the portfolio. Our results indicate that such strategy dominates both the
benchmark index and the uniformly weighted portfolio both in-sample and
out-of-sample.Comment: 11 pages, 3 figure
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