Reaching for yield from Yichuan Wang
This was a presentation on 'reaching for yield' that I prepared for my investment club, Michigan Interactive Investments. The argument is the same as in Jeremy Stein's speech. In low interest rate environments, pension fund managers go into riskier assets in order to reach their benchmark yields. Therefore, this should result in high beta stocks having lower risk-adjusted returns. I test this hypothesis using stock price data from the past five years on almost all stocks listed on the U.S. exchanges, and find that there did seem to be a substantial amount of 'reaching for yield' in 2011 and 2012, and that in 2011 this phenomenon was concentrated in the large cap stocks whereas now more of the effect seems to be with the small cap stocks.
While I'm not that confident on the exact quantitative magnitudes, reaching for yield does seem like a new channel through which nominal shocks can have real effects. Given the plausible assumption that most benchmarks are nominal in nature, then deflation has the added cost of inducing excess risk taking. This suggests that after financial crises, monetary policy should be even more aggressive in order to minimize the extent of the reach for yield.
I do think this investigation brings up an important methodological issue. I believe future macroeconomic research will rely much more heavily on observations from financial markets. This was the method recently used to measure the effect of sticky prices, and this technique of disaggregating financial data to lend support to macroeconomic theories is quite intriguing. The advantage that this has over traditional data sources is that you have a much higher frequency data stream in finance. This allows more in-depth analysis on focused time periods -- something that is much harder to do with regular CPI data.
In addition, through working on this I have found data analysis through open source R to be quite powerful. I generated all the plots in the presentation with the R package ggplot2, and all the stock price data came from Yahoo Finance interfaced through quantmod. Coupled with the powerful regression algorithms in R, I could generate the desired coefficients from weighted regressions and draw them on a plot.
This was a presentation on 'reaching for yield' that I prepared for my investment club, Michigan Interactive Investments. The argument is the same as in Jeremy Stein's speech. In low interest rate environments, pension fund managers go into riskier assets in order to reach their benchmark yields. Therefore, this should result in high beta stocks having lower risk-adjusted returns. I test this hypothesis using stock price data from the past five years on almost all stocks listed on the U.S. exchanges, and find that there did seem to be a substantial amount of 'reaching for yield' in 2011 and 2012, and that in 2011 this phenomenon was concentrated in the large cap stocks whereas now more of the effect seems to be with the small cap stocks.
While I'm not that confident on the exact quantitative magnitudes, reaching for yield does seem like a new channel through which nominal shocks can have real effects. Given the plausible assumption that most benchmarks are nominal in nature, then deflation has the added cost of inducing excess risk taking. This suggests that after financial crises, monetary policy should be even more aggressive in order to minimize the extent of the reach for yield.
I do think this investigation brings up an important methodological issue. I believe future macroeconomic research will rely much more heavily on observations from financial markets. This was the method recently used to measure the effect of sticky prices, and this technique of disaggregating financial data to lend support to macroeconomic theories is quite intriguing. The advantage that this has over traditional data sources is that you have a much higher frequency data stream in finance. This allows more in-depth analysis on focused time periods -- something that is much harder to do with regular CPI data.
In addition, through working on this I have found data analysis through open source R to be quite powerful. I generated all the plots in the presentation with the R package ggplot2, and all the stock price data came from Yahoo Finance interfaced through quantmod. Coupled with the powerful regression algorithms in R, I could generate the desired coefficients from weighted regressions and draw them on a plot.
Do you share the coding?
ReplyDeleteSure, just send me an email and I can send the code to you.
ReplyDelete