"Much of what we experience in life results from a combination of skill and luck." — From the Introduction
The trick, of course, is figuring out just how many of our successes (and failures) can be attributed to each — and how we can learn to tell the difference ahead of time.
This paper provides evidence of the real effects of financial markets. Using mutual fund redemptions as an instrument for price changes, we identify a strong effect of market prices on takeover activity (the "trigger effect"). An inter-quartile decrease in valuation leads to a 7 percentage point increase in acquisition likelihood, relative to a 6% unconditional takeover probability. Instrumentation addresses the fact that prices are endogenous and increase in anticipation of a takeover (the "anticipation effect").
Price momentum strategies have historically generated high positive returns with little systematic risk. However, these strategies also experience infrequent but severe losses. During 13 of the 978 months in our 1929–2010 sample, losses to a US-equity momentum strategy exceed 20 percent per month. We demonstrate that a hidden Markov model in which the market moves between latent "turbulent" and "calm" states in a systematic stochastic manner captures these high-loss episodes.
A set of recent papers attempts to explain the size and book-to-market anomalies with conditional CAPM or CCAPM models with economically motivated conditioning variables, or with factor models with economically motivated factors. The tests of these models, as presented, fail to reject the proposed model. We argue that these tests fail to reject the null hypothesis because they have very low statistical power against what we call the characteristics alternative.
We consider three "crisis shocks" related to key features of the 2007-2008 crisis, for emerging and developed economies: (1) the collapse of global trade, (2) the contraction of credit supply, and (3) selling pressure on firms' equity. Using an international cross-section of firms, we find that returns' sensitivities to these shocks imply large and statistically significant influences on residual equity returns during the crisis period (after controlling for normal risk factors that are associated with expected returns).
The 2007-2009 financial crisis that started in the summer of 2007 had its origins in the US housing policies, the subprime mortgage market in particular, and the end of the real estate bubble in the US. Careful consideration of the causes, consequences and policy responses suggest that various factors contributed to the severity of the 2007-08 crisis, and experts disagree about the weights to attach to each in explaining what is now regarded as the most significant economic contraction since the Great Depression.
Sovereign Wealth Funds (SWFs) are state-owned investment funds with combined asset holdings that are fast approaching four trillion dollars. Recently emerging as a major force in global financial markets, SWFs have other distinctive features besides their state-owned status: they are mainly located in developing countries and are intimately tied to energy and commodities exports, and they carry virtually no liabilities and have little redemption risk, which allows them to take a longer-term investment outlook than most other institutional investors.
Defaults arising from illiquidity can lead to private workouts, formal bankruptcy proceedings or even liquidation. All these outcomes can result in deadweight losses. Corporate illiquidity in the presence of realistic capital market frictions can be managed by a) equity dilution, b) carrying positive cash balances, or c) entering into loan commitments with a syndicate of lenders. An efficient way to manage illiquidity is to rely on mechanisms that transfer cash from "good states" into "bad states" (i.e., financial distress) without wasting liquidity in the process.
Particle learning provides a simulation-based approach to sequential Bayesian computation. To sample from a posterior distribution of interest we use an essential state vector together with a predictive and propagation rule to build a resampling-sampling framework. Predictive inference and sequential Bayes factors are a direct by-product. Our approach provides a simple yet powerful framework for the construction of sequential posterior sampling strategies for a variety of commonly used models.