In the last third installment, we will finish exploring the world of market timing strategies (see parts 1 & 2). We will focus on yield curve predictors and incorporate all three ideas (price-based, macro-economic, and yield curve predictors) into one final trading strategy that yields an annual return above that of the stock market while doubling its Sharpe ratio and reducing maximal drawdown by two thirds.

In this second installment in a series of three articles, we will continue with our goal to construct a market timing strategy that would sidestep the equity market during bear markets. A few days ago, we started with price-based market timing strategies. Today, we will focus on macroeconomic indicators and predictors derived from the movements in the commodity markets.

In this series of three articles, our goal is to construct a market timing strategy that would reliably sidestep the equity market during bear markets, thereby reducing market volatility and boosting risk-adjusted returns. We will build trading signals based on price-based indicators, macroeconomic indicators, and a leading indicator, a yield curve, that would try to predict recessions and bear markets in advance. All three articles would be published in a span of the next few days. We start with the first part – a short intro into the market timing strategies using price-based rules.

Arbitrage is a central concept in finance. It is defined as simultaneous long and short positions in similar assets to exploit mispricing. Hedge funds experienced fast growth over the past three decades, as real-world arbitrageurs as a group. As they increasingly influence the financial market, it is important to understand the economic drivers of hedge fund returns. Therefore we would like to present a paper dealing with the development of a parsimonious factor model, based on anomalies, to explain hedge fund returns.

Hello all,

What have we accomplished in the last month?

– A new Quantpedia Pro report – the Strategy Grading
– 10 new Quantpedia Premium strategies have been added to our database
– 11 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 7 new backtests written in QuantConnect code
– And finally, 3+2 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

Factor investing and detailed allocation according to different sets of factors are lively researched topics with many unanswered and open questions. Many views are often conflicting and from both radical sides — on one, that only a few factors should be necessary to explain the cross-section of mean returns, which is attractive, especially because of its simplicity; on the other, that you can use complex (authors examine the 161 “clear predictors” and 44 “likely predictors”) combinations of factors from less known and unorthodox models, but falling into dangerous and often unexamined “factor zoo” with many undesirable, unexamined and non-controllable outcomes. A huge gap is often seen in finance between the theory of academia and practical applications (by PMs [portfolio managers]), and so is especially present in this one. Let’s take a look at what the complexity of factors does for various equities pricing models.