144 research outputs found
Speculative Pressure
The paper investigates the information content of speculative pressure across futures classes. Long-short portfolios of futures contracts sorted by speculative pressure capture a significant premium in commodity, currency and equity markets but not in fixed income markets. Exposure to commodity, currency and equity index futures’ speculative pressure is priced in the broad cross-section after controlling for momentum, carry, global liquidity and volatility risks. The findings are confirmed by robustness tests using alternative speculative pressure signals, portfolio construction techniques and sub-periods inter alia. We argue that there is an efficient hedgers-speculators risk transfer in commodity, currency and equity index futures markets
Twitter mood predicts the stock market
Behavioral economics tells us that emotions can profoundly affect individual
behavior and decision-making. Does this also apply to societies at large, i.e.,
can societies experience mood states that affect their collective decision
making? By extension is the public mood correlated or even predictive of
economic indicators? Here we investigate whether measurements of collective
mood states derived from large-scale Twitter feeds are correlated to the value
of the Dow Jones Industrial Average (DJIA) over time. We analyze the text
content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder
that measures positive vs. negative mood and Google-Profile of Mood States
(GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital,
Kind, and Happy). We cross-validate the resulting mood time series by comparing
their ability to detect the public's response to the presidential election and
Thanksgiving day in 2008. A Granger causality analysis and a Self-Organizing
Fuzzy Neural Network are then used to investigate the hypothesis that public
mood states, as measured by the OpinionFinder and GPOMS mood time series, are
predictive of changes in DJIA closing values. Our results indicate that the
accuracy of DJIA predictions can be significantly improved by the inclusion of
specific public mood dimensions but not others. We find an accuracy of 87.6% in
predicting the daily up and down changes in the closing values of the DJIA and
a reduction of the Mean Average Percentage Error by more than 6%
WARNING: Physics Envy May Be Hazardous To Your Wealth!
The quantitative aspirations of economists and financial analysts have for
many years been based on the belief that it should be possible to build models
of economic systems - and financial markets in particular - that are as
predictive as those in physics. While this perspective has led to a number of
important breakthroughs in economics, "physics envy" has also created a false
sense of mathematical precision in some cases. We speculate on the origins of
physics envy, and then describe an alternate perspective of economic behavior
based on a new taxonomy of uncertainty. We illustrate the relevance of this
taxonomy with two concrete examples: the classical harmonic oscillator with
some new twists that make physics look more like economics, and a quantitative
equity market-neutral strategy. We conclude by offering a new interpretation of
tail events, proposing an "uncertainty checklist" with which our taxonomy can
be implemented, and considering the role that quants played in the current
financial crisis.Comment: v3 adds 2 reference
The non-random walk of stock prices: The long-term correlation between signs and sizes
We investigate the random walk of prices by developing a simple model
relating the properties of the signs and absolute values of individual price
changes to the diffusion rate (volatility) of prices at longer time scales. We
show that this benchmark model is unable to reproduce the diffusion properties
of real prices. Specifically, we find that for one hour intervals this model
consistently over-predicts the volatility of real price series by about 70%,
and that this effect becomes stronger as the length of the intervals increases.
By selectively shuffling some components of the data while preserving others we
are able to show that this discrepancy is caused by a subtle but long-range
non-contemporaneous correlation between the signs and sizes of individual
returns. We conjecture that this is related to the long-memory of transaction
signs and the need to enforce market efficiency.Comment: 9 pages, 5 figures, StatPhys2
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