53,889 research outputs found
Predicting the Effects of News Sentiments on the Stock Market
Stock market forecasting is very important in the planning of business
activities. Stock price prediction has attracted many researchers in multiple
disciplines including computer science, statistics, economics, finance, and
operations research. Recent studies have shown that the vast amount of online
information in the public domain such as Wikipedia usage pattern, news stories
from the mainstream media, and social media discussions can have an observable
effect on investors opinions towards financial markets. The reliability of the
computational models on stock market prediction is important as it is very
sensitive to the economy and can directly lead to financial loss. In this
paper, we retrieved, extracted, and analyzed the effects of news sentiments on
the stock market. Our main contributions include the development of a sentiment
analysis dictionary for the financial sector, the development of a
dictionary-based sentiment analysis model, and the evaluation of the model for
gauging the effects of news sentiments on stocks for the pharmaceutical market.
Using only news sentiments, we achieved a directional accuracy of 70.59% in
predicting the trends in short-term stock price movement.Comment: 4 page
The Discreet Trader
This paper examines insider trading, specifically trades by corporate insiders around quarterly earnings announcements. Announcements were broken up into three categories: earnings above analyst expectations, earnings below expectations, and earnings in line with expectations. Trade data was collected from the thirty companies of the Dow Jones Industrial Average from 2012-’13. The trades were sorted by purchases and sales by date and analyzed with the earnings report of which the trades were made. Only trades in the interval from twenty days before the announcement date to twenty days after the announcement date were considered. The prediction was that corporate insiders would leverage their inside knowledge to delay trading until after the earnings announcement. They would benefit financially by trading after the announcement and draw less attention from the SEC, as they delayed trading until the announcement became public information. However, knowing how the market would react would allow them to make a meditated decision. For an announcement that was below analyst expectations, corporate insiders should buy stock after the market reaction causes the price to drop. Our findings were that corporate insiders did in fact wait until the announcement day and overall were net buyers. The study will give better insights into how corporate insiders trade and how restrictions can be made to stop this insider trading activity
Testing for financial crashes using the Log Periodic Power Law mode
A number of papers claim that a Log Periodic Power Law (LPPL) fitted to
financial market bubbles that precede large market falls or 'crashes', contain
parameters that are confined within certain ranges. The mechanism that has been
claimed as underlying the LPPL, is based on influence percolation and a
martingale condition. This paper examines these claims and the robustness of
the LPPL for capturing large falls in the Hang Seng stock market index, over a
30-year period, including the current global downturn. We identify 11 crashes
on the Hang Seng market over the period 1970 to 2008. The fitted LPPLs have
parameter values within the ranges specified post hoc by Johansen and Sornette
(2001) for only seven of these crashes. Interestingly, the LPPL fit could have
predicted the substantial fall in the Hang Seng index during the recent global
downturn. We also find that influence percolation combined with a martingale
condition holds for only half of the pre-crash bubbles previously reported.
Overall, the mechanism posited as underlying the LPPL does not do so, and the
data used to support the fit of the LPPL to bubbles does so only partially.Comment: 24 pages, 5 figure
Heterogeneous Agents Models: two simple examples, forthcoming In: Lines, M. (ed.) Nonlinear Dynamical Systems in Economics, CISM Courses and Lectures, Springer, 2005, pp.131-164.
These notes review two simple heterogeneous agent models in economics and finance. The first is a cobweb model with rational versus naive agents introduced in Brock and Hommes (1997). The second is an asset pricing model with fundamentalists versus technical traders introduced in Brock and Hommes (1998). Agents are boundedly rational and switch between different trading strategies, based upon an evolutionary fitness measure given by realized past profits. Evolutionary switching creates a nonlinearity in the dynamics. Rational routes to randomness, that is, bifurcation routes to complicated dynamical behaviour occur when agents become more sensitive to differences in evolutionary fitness.
The fine-structure of volatility feedback I: multi-scale self-reflexivity
We attempt to unveil the fine structure of volatility feedback effects in the
context of general quadratic autoregressive (QARCH) models, which assume that
today's volatility can be expressed as a general quadratic form of the past
daily returns. The standard ARCH or GARCH framework is recovered when the
quadratic kernel is diagonal. The calibration of these models on US stock
returns reveals several unexpected features. The off-diagonal (non ARCH)
coefficients of the quadratic kernel are found to be highly significant both
In-Sample and Out-of-Sample, but all these coefficients turn out to be one
order of magnitude smaller than the diagonal elements. This confirms that daily
returns play a special role in the volatility feedback mechanism, as postulated
by ARCH models. The feedback kernel exhibits a surprisingly complex structure,
incompatible with models proposed so far in the literature. Its spectral
properties suggest the existence of volatility-neutral patterns of past
returns. The diagonal part of the quadratic kernel is found to decay as a
power-law of the lag, in line with the long-memory of volatility. Finally,
QARCH models suggest some violations of Time Reversal Symmetry in financial
time series, which are indeed observed empirically, although of much smaller
amplitude than predicted. We speculate that a faithful volatility model should
include both ARCH feedback effects and a stochastic component
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