Essays on Financial Applications of Nonlinear Models
Date
2017
Authors
Wang, Wanbin
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Canberra, ACT : The Australian National University
Abstract
In this thesis, we examine the relationship between news and the
stock market. Further, we explore methods and build new nonlinear
models for forecasting stock price movement and portfolio
optimization based on past stock prices and on one type of big
data, news items, which are obtained through the RavenPack News
Analytics Global Equities editions.
The thesis consists of three essays. In Essay 1, we investigate
the relationship between news items and stock prices using the
artificial neural network (ANN) model. First, we use Granger
causality to ascertain how news items affect stock prices. The
results show that news volume is not the Granger cause of stock
price change; rather, news sentiment is. Second, we test the
semi–strong form efficient market hypothesis, whereas most
existing research testing efficient market hypothesis focuses on
the weak–form version. Our ANN strategies consistently
outperform the passive buy–and–hold strategy and this finding
is apparently at odds with the notion of the efficient market
hypothesis. Finally, using news sentiment analytics from
RavenPack Dow Jones News Analytics, we show positive
profitability with out–of–sample prediction using the
proposed ANN strategies for Google Inc. (NASDAQ: GOOG).
In Essay 2, we expand the utility of the information from news
volume and news sentiments to encompass portfolio
diversification. For the Dow Jones Industrial Average (DJIA)
components, we assign different weights to build portfolios
according to their weekly news volumes or news sentiments. Our
results show that news volume contributes to portfolio variance
both in–sample and out–of–sample: positive news sentiment
contributes to the portfolio return in–sample, while negative
contributes to the portfolio return out–of–sample, which is a
consequence of investors overreacting to the news sentiment.
Further, we propose a novel approach to portfolio diversification
using the k–Nearest Neighbors (kNN) algorithm based on the idea
that news sentiment correlates with stock returns.
Out–of–sample results indicate that such strategy dominates
the benchmark DJIA index portfolio.
In Essay 3, we propose a new model called the Combined Markov and
Hidden Markov Model (CMHMM), in which observation is affected by
a Markov model and an HMM (Hidden Markov Model) model. The three
fundamental questions of the CMHMM are discussed. Further, the
application of the CMHMM, in which the news sentiment is one
observation and the stock return is the other, is discussed. The
empirical results of the trading strategy based on the CMHMM show
the potential applications of the proposed model in finance.
This thesis contributes to the literature in a number of ways.
First, it extends the literature on financial applications of
nonlinear models. We explore the applications of the ANNs and kNN
in the financial market. Besides, the proposed new CMHMM model
adheres to the nature of the stock market and has better
potential prediction ability. Second, the empirical results from
this dissertation contribute to the understanding of the
relationship between news and the stock market. For instance, our
research found that news volume contributes to the portfolio
return and that investors overreact to news sentiment—a
phenomenon that has been discussed by other scholars from
different angles.
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Keywords
Nonlinear Models, news sentiment, artificial neural network, Hidden Markov Model
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