Financial diversification before modern portfolio theory: UK financial advice documents in the late nineteenth and the beginning of the twentieth century

The paper offers textual evidence from a series of financial advice documents in the late nineteenth century and the early twentieth century of how UK investors perceived of and managed risk. In the world’s largest financial centre of the time, UK investors were familiar with the concept of correlation and financial advisers’ suggestions were consistent with the recommendations of modern portfolio theory in relation to portfolio selection strategies. From the 1870s, there was an increased awareness of the benefits of financial diversification - primarily putting equal amounts into a number of different securities - with much of the emphasis being on geographical rather than sectoral diversification and some discussion of avoiding highly correlated investments. Investors in the past were not so naïve as mainstream financial discussions suggest today

(WP 2014-01) Is Bitcoin the \u27Paris Hilton\u27 of the Currency World? Or Are the Early Investors onto Something That Will Make Them Rich? [updated version]

The bitcoin phenomenon, and the technological innovation that made it possible, is interesting - but for investors large and small, the more pertinent question is whether they should buy the digital currency or avoid it. We analyze a bitcoin investment from the standpoint of an investor with a diversified portfolio using both in-sample and out-of-sample settings. Within the in-sample setting, bitcoin does not yield added value to investors with utility function consistent with the mean-variance setting. On the other hand, they do offer diversification benefits to investors with negative exponential and power utility functions. However, these benefits are not preserved in the out-of-sample framework. In most cases, the optimal portfolios that include only the traditional asset classes appear to have superior performance

Multi-Period Trading via Convex Optimization

We consider a basic model of multi-period trading, which can be used to evaluate the performance of a trading strategy. We describe a framework for single-period optimization, where the trades in each period are found by solving a convex optimization problem that trades off expected return, risk, transaction cost and holding cost such as the borrowing cost for shorting assets. We then describe a multi-period version of the trading method, where optimization is used to plan a sequence of trades, with only the first one executed, using estimates of future quantities that are unknown when the trades are chosen. The single-period method traces back to Markowitz; the multi-period methods trace back to model predictive control. Our contribution is to describe the single-period and multi-period methods in one simple framework, giving a clear description of the development and the approximations made. In this paper we do not address a critical component in a trading algorithm, the predictions or forecasts of future quantities. The methods we describe in this paper can be thought of as good ways to exploit predictions, no matter how they are made. We have also developed a companion open-source software library that implements many of the ideas and methods described in the paper

Global Asset Return in Pension Funds: a dynamical risk analysis

The aim of the paper is to develop a technique for rebalancing pension fund portfolios in function of their pointwise level of risk. The performance of pension funds is often measured by their global asset returns because of the latter’s influence on periodic contributions and/or future benefits. However, in periods of market crisis attention is focused on the risk level given their social security (and not speculative) function. We describe the process of the global asset return by a multifractional Brownian motion using the function H(t) to detect high or low volatility phases. A procedure is carried out to balance the asset composition when the established local degree of risk is exceeded. The application is carried out on portfolios obtained in accordance with Italian regulations regarding investment limits.Pension Funds, risk control, multifractional Brownian motion

ALM practices, multiple uncertainty and monopolistic behavior: A microeconomic study of banking decisions

We study the decisions that a monopolistic bank takes to achieve risk management and profit objectives. The bank faces liquidity and solvency risks because loans may not be repaid and because unexpected deposit withdrawals may occur. The Asset-Liability-Management (ALM) banking model shows that compromise solutions are necessary to deal with the tradeoffs between liquidity management and profitability. It also shows that asset management practices increase profits. Moreover it shows that liability management practices and market power support profitability. Finally, the model confirms that banks should undertake long-term risky investments when depositors trust the viability of the asset transformation process.Banking; ALM; multiple uncertainty; monopolistic behavior