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"INVESTMENT STRATEGY"
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Crypto investing guide : how to invest in Bitcoin, DeFi, NFTs, and more
\"What if we told you that one book could contain an entire education in crypto investing topics? Whether you're an uninitiated newbie or an established veteran, this book exists to help you get a profitable start as a new crypto investor. The committed reader will go on an educational journey that starts in the world of conventional finance before crossing the crypto bridge to go deep on crypto assets, decentralized finance, NFTs, and security token offerings. This book is your one-stop shop on building a deadly working knowledge of the crypto markets and our ideas on how to play them profitably. It's time for the wall of technical smoke and mirrors around crypto to come down, and this book represents an experienced technical team sharing its hard-won knowledge as accessible as possible. You don't need to be a math genius to invest in crypto successfully. But you do need a strong base of knowledge to work from. This book is your foundation\" -- Page [4] of cover.
Book
Multivariate Gaussian and Student-t process regression for multi-output prediction
Gaussian process model for vector-valued function has been shown to be useful for multi-output prediction. The existing method for this model is to reformulate the matrix-variate Gaussian distribution as a multivariate normal distribution. Although it is effective in many cases, reformulation is not always workable and is difficult to apply to other distributions because not all matrix-variate distributions can be transformed to respective multivariate distributions, such as the case for matrix-variate Student-
t
distribution. In this paper, we propose a unified framework which is used not only to introduce a novel multivariate Student-
t
process regression model (MV-TPR) for multi-output prediction, but also to reformulate the multivariate Gaussian process regression (MV-GPR) that overcomes some limitations of the existing methods. Both MV-GPR and MV-TPR have closed-form expressions for the marginal likelihoods and predictive distributions under this unified framework and thus can adopt the same optimization approaches as used in the conventional GPR. The usefulness of the proposed methods is illustrated through several simulated and real-data examples. In particular, we verify empirically that MV-TPR has superiority for the datasets considered, including air quality prediction and bike rent prediction. At last, the proposed methods are shown to produce profitable investment strategies in the stock markets.
Journal Article
Equilibrium reinsurance-investment strategies with partial information and common shock dependence
In this paper, we study an optimal reinsurance-investment problem with partial information and common shock dependence under the mean-variance criterion for an insurer. The insurer has two dependent classes of insurance business, which are subject to a common shock. We consider the optimal reinsurance-investment problem under complete information and partial information, respectively. We formulate the complete information problem within a game theoretic framework and seek the equilibrium reinsurance-investment strategy and equilibrium value function by solving an extended Hamilton–Jacobi–Bellman system of equations. For the partial information problem, we first transform it to a completely observable model by virtue of the filtering theory, then derive the equilibrium strategy and equilibrium value function by using the methods similar to those for the complete information problem. In addition, we illustrate the equilibrium reinsurance-investment strategies by numerical examples and discuss the impacts of model parameters on the equilibrium reinsurance-investment strategies for both the complete information and partial information cases.
Journal Article
Can capital markets identify heterogeneous environmental investment strategies of firms? Evidence from China
Using a 2005–2020 sample of A-share listed companies in China’s heavily polluting industries, this paper divides environmental investment strategies into “light green,” “medium green,” and “deep green” dimensions and constructs a panel threshold model to investigate the impact of different environmental strategies on China’s stock market. The study found that environmental investment intensity has a double threshold effect on stock returns, “medium green” behavior helps improve stock returns, and “light green” and “deep green” behaviors are not conducive to stock returns. Institutional investors are more accurate than ordinary investors in identifying heterogeneous environmental strategies. The mechanism test shows that different environmental strategies affect stock returns through internal “value enhancement” and external “government subsidy” mechanisms. Moreover, “greenwashing” benefits for companies are short-lived; the market eventually imposes punitive pricing. These findings provide a reference for enterprise- and market-oriented green development systems.
Journal Article
Applying Hybrid ARIMA-SGARCH in Algorithmic Investment Strategies on S&P500 Index
This research aims to compare the performance of ARIMA as a linear model with that of the combination of ARIMA and GARCH family models to forecast S&P500 log returns in order to construct algorithmic investment strategies on this index. We used the data collected from Yahoo Finance with daily frequency for the period from 1 January 2000 to 31 December 2019. By using a rolling window approach, we compared ARIMA with the hybrid models to examine whether hybrid ARIMA-SGARCH and ARIMA-EGARCH can really reflect the specific time-series characteristics and have better predictive power than the simple ARIMA model. In order to assess the precision and quality of these models in forecasting, we compared their equity lines, their forecasting error metrics (MAE, MAPE, RMSE, MAPE), and their performance metrics (annualized return compounded, annualized standard deviation, maximum drawdown, information ratio, and adjusted information ratio). The main contribution of this research is to show that the hybrid models outperform ARIMA and the benchmark (Buy&Hold strategy on S&P500 index) over the long term. These results are not sensitive to varying window sizes, the type of distribution, and the type of the GARCH model.
Journal Article
Compensation for girls in early childhood and its long-run impact: family investment strategies under rainfall shocks
This study explores the effect of early-life rainfall shocks on the long-run performance of children of different genders and possible mechanisms. Using data from a nationwide Chinese survey, we find that rainfall in children’s year of birth increase girls’ long-term test scores and educational attainment, whereas boys are unaffected. Our analysis of potential mechanisms indicates that birthyear rainfall leads to longer duration of breastfeeding for girls. We also find that, in years with more rainfall, mothers work fewer hours on agricultural work and parents are less likely to agree that raising children is to provide support for themselves in old age. Further analysis shows that rainfall in the current year disproportionately affects private health insurance for girls, indicating that positive rainfall shocks are associated with an increased willingness to invest in girls.
Journal Article
Evolution in pecunia
The paper models evolution in pecunia—in the realm of finance. Financial markets are explored as evolving biological systems. Diverse investment strategies compete for the market capital invested in long-lived dividend-paying assets. Some strategies survive and some become extinct. The basis of our paper is that dividends are not exogenous but increase with the wealth invested in an asset, as is the case in a production economy. This might create a positive feedback loop in which more investment in some asset leads to higher dividends which in turn lead to higher investments. Nevertheless, we are able to identify a unique evolutionary stable investment strategy. The problem is studied in a framework combining stochastic dynamics and evolutionary game theory. The model proposed employs only objectively observable market data, in contrast with traditional settings relying upon unobservable investors’ characteristics (utilities and beliefs). Our method is analytical and based on mathematical reasoning. A numerical illustration of the main result is provided.
Journal Article
Asset allocation of Australian superannuation funds: a markov regime switching approach
We extend an observable Markov Regime Switching framework to assess the switching behaviour of asset classes of Australian superannuation funds across different fund sizes. We identify the most prominent asset class which contributes to the performance of the investment options and what factors trigger funds’ decisions on rebalancing their portfolio. We find that smaller funds tend to be more active in switching to aggressive options and the larger funds are more conservative. However, in periods of volatility, the large funds are the risk seekers and tend to switch their asset classes and hence their investment strategies. The asset classes whose values add to the performance of the investment options are equity markets and bond markets with the domestic equity market having better performance than international equity market. The switch for the larger funds is driven by volatility of the equity market.
Journal Article
Supervised autoencoder MLP for financial time series forecasting
This paper investigates the enhancement of financial time series forecasting with the use of neural networks through supervised autoencoders, aiming to improve investment strategy performance. It specifically examines the impact of noise augmentation and triple barrier labeling on risk-adjusted returns, using the Sharpe and Information Ratios. The study focuses on the S&P 500 index, EUR/USD, and BTC/USD as the traded assets from January 1, 2016, to April 30, 2022. Findings indicate that supervised autoencoders, with balanced noise augmentation and bottleneck size, significantly boost strategy effectiveness. However, excessive noise and large bottleneck sizes can impair performance, highlighting the importance of precise parameter tuning. This paper also presents a derivation of a novel optimization metric that can be used with triple barrier labeling. The results of this study have substantial policy implications, suggesting that financial institutions and regulators could leverage techniques presented to enhance market stability and investor protection, while also encouraging more informed and strategic investment approaches in various financial sectors.
Journal Article
Capital asset pricing model in insurance risk theory
One of the latest trends in the insurance business is the development of investment strategies to increase capital. The use of profit from financial investments can significantly enhance the assessment of insurance risk. In this paper, a combined model between the Capital Asset Valuation Model and the classical risk theory in the form of the Cramer Lundberg model is considered.
Journal Article