8,056 research outputs found
Heat waves or Meteor showers: Empirical evidence from the stock markets
In order to study the volatility spillovers / the transfer of volatilities from spot and futures markets for the period 1st January 2001 to 30th November 2005 with high frequency data i.e., one minute intervals, we have used GARCH models to compute volatilities and VAR models for the returns of different markets and for the volatilities. It is evident that, these VAR models for the volatilities can exhibit the nature of the change in volatility. In a heat wave, the conditional variance of the returns in spot (futures) market depends only upon the past shocks in the given market. For meteor showers, the impact of shocks on spot (futures) markets are transferred from other i.e., futures (spot) markets. With the VAR (1)-GARCH (1,1) analysis, we found that both series are I(1) and that a bi-directional relationship exists between the spot and future market return series. Empirically it is evident that both heat waves and meteor showers exist in Indian spot and futures markets.Volatility Spillovers, Heat Waves, Meteor Showers, Indian Stock Market.
A Comparative Analysis of Portfolio Optimization Using Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian Stock Market
This paper presents a comparative analysis of the performances of three
portfolio optimization approaches. Three approaches of portfolio optimization
that are considered in this work are the mean-variance portfolio (MVP),
hierarchical risk parity (HRP) portfolio, and reinforcement learning-based
portfolio. The portfolios are trained and tested over several stock data and
their performances are compared on their annual returns, annual risks, and
Sharpe ratios. In the reinforcement learning-based portfolio design approach,
the deep Q learning technique has been utilized. Due to the large number of
possible states, the construction of the Q-table is done using a deep neural
network. The historical prices of the 50 premier stocks from the Indian stock
market, known as the NIFTY50 stocks, and several stocks from 10 important
sectors of the Indian stock market are used to create the environment for
training the agent.Comment: The report is 52 pages long. It is based on the capstone project done
in the post graduate course of data science in Praxis Business School,
Kolkata, India, of the Autumn Batch, 202
A Comparative Study of Portfolio Optimization Methods for the Indian Stock Market
This chapter presents a comparative study of the three portfolio optimization
methods, MVP, HRP, and HERC, on the Indian stock market, particularly focusing
on the stocks chosen from 15 sectors listed on the National Stock Exchange of
India. The top stocks of each cluster are identified based on their free-float
market capitalization from the report of the NSE published on July 1, 2022 (NSE
Website). For each sector, three portfolios are designed on stock prices from
July 1, 2019, to June 30, 2022, following three portfolio optimization
approaches. The portfolios are tested over the period from July 1, 2022, to
June 30, 2023. For the evaluation of the performances of the portfolios, three
metrics are used. These three metrics are cumulative returns, annual
volatilities, and Sharpe ratios. For each sector, the portfolios that yield the
highest cumulative return, the lowest volatility, and the maximum Sharpe Ratio
over the training and the test periods are identified.Comment: This is the draft version of the chapter that has been accepted for
publication in the edited volume titled "Data Science: Theory and Practice".
The volume is edited by Jaydip Sen and Sayantani Roy Choudury and will be
published by IntechOpen, London, UK. The chapter is 74 pages long and it
contains 32 tables and 62 figure
Portfolio Optimization: A Comparative Study
Portfolio optimization has been an area that has attracted considerable
attention from the financial research community. Designing a profitable
portfolio is a challenging task involving precise forecasting of future stock
returns and risks. This chapter presents a comparative study of three portfolio
design approaches, the mean-variance portfolio (MVP), hierarchical risk parity
(HRP)-based portfolio, and autoencoder-based portfolio. These three approaches
to portfolio design are applied to the historical prices of stocks chosen from
ten thematic sectors listed on the National Stock Exchange (NSE) of India. The
portfolios are designed using the stock price data from January 1, 2018, to
December 31, 2021, and their performances are tested on the out-of-sample data
from January 1, 2022, to December 31, 2022. Extensive results are analyzed on
the performance of the portfolios. It is observed that the performance of the
MVP portfolio is the best on the out-of-sample data for the risk-adjusted
returns. However, the autoencoder portfolios outperformed their counterparts on
annual returns.Comment: This is the preprint of the book chapter accepted for publication in
the book titled "Deep Learning - Recent Finding and Researches" edited by
Manuel Dom\'inguez-Morales. The book is scheduled to be be published by
IntechOpen, London, UK in January 2024. This is not the final version of the
chapte
Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods
We quantify the amount of information filtered by different hierarchical
clustering methods on correlations between stock returns comparing it with the
underlying industrial activity structure. Specifically, we apply, for the first
time to financial data, a novel hierarchical clustering approach, the Directed
Bubble Hierarchical Tree and we compare it with other methods including the
Linkage and k-medoids. In particular, by taking the industrial sector
classification of stocks as a benchmark partition, we evaluate how the
different methods retrieve this classification. The results show that the
Directed Bubble Hierarchical Tree can outperform other methods, being able to
retrieve more information with fewer clusters. Moreover, we show that the
economic information is hidden at different levels of the hierarchical
structures depending on the clustering method. The dynamical analysis on a
rolling window also reveals that the different methods show different degrees
of sensitivity to events affecting financial markets, like crises. These
results can be of interest for all the applications of clustering methods to
portfolio optimization and risk hedging.Comment: 31 pages, 17 figure
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