Social scientists often estimate models from correlational data, where the independent variable has not been exogenously manipulated; they also make implicit or explicit causal claims based on these models. When can these claims be made? We answer this question by first discussing design and estimation conditions under which model estimates can be interpreted, using the randomized experiment as the gold standard. We show how endogeneity – which includes omitted variables, omitted selection, simultaneity, common-method variance, and measurement error – renders estimates causally uninterpretable. Second, we present methods that allow researchers to test causal claims in situations where randomization is not possible or when causal interpretation could be confounded; these methods include fixed-effects panel, sample selection, instrumental variable, regression discontinuity, and difference-in-differences models. Third, we take stock of the methodological rigor with which causal claims are being made in a social sciences discipline by reviewing a representative sample of 110 articles on leadership published in the previous 10 years in top-tier journals. Our key finding is that researchers fail to address at least 66% and up to 90% of design and estimation conditions that make causal claims invalid. We conclude by offering 10 suggestions on how to improve non-experimental research.

On making causal claims : A review and recommendations

In recent years, the tendency of the number of financial institutions including cryptocurrencies in their portfolios has accelerated. Cryptocurrencies are the first pure digital assets to be included by asset managers. Although they have some commonalities with more traditional assets, they have their own separate nature and their behaviour as an asset is still in the process of being understood. It is therefore important to summarise existing research papers and results on cryptocurrency trading, including available trading platforms, trading signals, trading strategy research and risk management. This paper provides a comprehensive survey of cryptocurrency trading research, by covering 146 research papers on various aspects of cryptocurrency trading (e.g., cryptocurrency trading systems, bubble and extreme conditions, prediction of volatility and return, crypto-assets portfolio construction and crypto-assets, technical trading and others). This paper also analyses datasets, research trends and distribution among research objects(contents/properties) and technologies, concluding with some promising opportunities that remain open in cryptocurrency trading.

Cryptocurrency Trading: A Comprehensive Survey.

By the end of 2017, 27 cryptocurrencies topped a market capitalization of one billion USD. Bitcoin is still shaping market and media coverage, however, recently we faced a vibrant rise of other currencies. As a result, 2017 has also witnessed the advent of a large number of cryptocurrency-funds. In this paper, we use Markowitz' mean-variance framework in order to assess risk-return-benefits of cryptocurrency-portfolios. We relate risk and return of different portfolio strategies to single cryptocurrency investments. In an out-of-sample analysis accounting for transaction cost we find that combining cryptocurrencies in a portfolio enriches the set of 'low'-risk cryptocurrency investment opportunities.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID3124832_code392030.pdf?abstractid=3124832&mirid=1

Cryptocurrency-Portfolios in a Mean-Variance Framework

A systematic review of the bubble dynamics of cryptocurrency prices

Bitcoin is designed as a peer-to-peer cash system. To work as a currency, it must be stable or be backed by a government. In this paper, we show that the volatility of Bitcoin prices is extreme and almost 10 times higher than the volatility of major exchange rates (US dollar against the euro and the yen). The excess volatility even adversely affects its potential role in portfolios. Our analysis implies that Bitcoin cannot function as a medium of exchange and has only limited use as a risk-diversifier. In contrast, we use the deflationary design of Bitcoin as a theoretical basis and demonstrate that Bitcoin displays store of value characteristics over long horizons.

/pdf/the-volatility-of-bitcoin-and-its-role-as-a-medium-of-19p861mbi3.pdf

The volatility of Bitcoin and its role as a medium of exchange and a store of value.

On the investment credentials of Bitcoin: A cross-currency perspective

Corporate cash holdings and promoter ownership

Achievement of diversification gains is one of the most important objectives of portfolio optimization. We aim to ascertain the magnitude and robustness of diversification gains for an already diversified portfolio due to inclusion of Bitcoin from the standpoint of an Indian investor. We use eight indices to build a portfolio spread across six asset classes, namely, equity, fixed income, commodities, real estate, gold and alternative investments. All asset classes except alternative investments are represented by indices which track performance of respective assets in India. We employ three investment strategies, namely, ‘long only’, ‘constrained’ and ‘equally weighted’ to construct optimal portfolios. Instead of the traditional mean-variance optimization methodology, we apply mean-CVaR approach for optimal allocation of weights to assets. Our findings suggest that portfolios containing Bitcoin have superior risk-adjusted returns as compared to portfolios without Bitcoin for two strategies, ‘long only’ and ‘equally weighted’. Results suggest relatively stable weights across the investment horizon for Bitcoin in ‘long only’ strategy as compared to ‘constrained’ framework. The findings are relevant for institutional and retail investors looking to earn higher risk-adjusted returns for a portfolio denominated in Indian rupee. The study highlights the immense potential of Bitcoin as an investment alternative and also adds fresh evidence to the scarce literature on Bitcoin from a financial perspective.

Bitcoin and Portfolio Diversification: Evidence from India

We examine the evolution and determinants of corporate cash holdings of Indian non-financial firms for the period 2001-2016. In contrast to a prominent increase in corporate liquidity levels globally after the global financial crisis, we document a gradual decline in corporate cash holdings of Indian non-financial firms. We provide empirical evidence to show that profitability, operating cash flow and cash flow volatility are positively associated with cash holdings. Further, our results suggest that size,leverage, liquidity, capital expenditure and promoter ownership share a negative relationship with cash holdings. In addition, we do not find concrete empirical evidence regarding relationship of corporate cash holdings with growth potential and dividend payments. Our results are broadly consistent across five panel data estimation techniques namely pooled ordinary least squares regression, Fama-MacBeth procedure, fixed effects estimator, between effects estimator and system generalised method of moments estimator. Moreover, we use an alternative proxy of corporate cash holdings to reinforce the robustness of our findings. Finally, our results show that a typical Indian non-financial firm achieves target cash levels in a time span of 2.18 years on an average, thereby highlighting a slower speed of adjustment of corporate cash holdings as compared to firms in developed economies.

/pdf/determinants-of-corporate-cash-holdings-evidence-from-india-2a3b2vh4yz.pdf

Prateek Bedi

Papers

On the investment credentials of Bitcoin: A cross-currency perspective

Corporate cash holdings and promoter ownership

Bitcoin and Portfolio Diversification: Evidence from India

Determinants of Corporate Cash Holdings: Evidence from India