Determine which ratios measurement affected the Risk Adjusted returns for different classes of assets based on Year to Date RETURNS. Data downloaded from http://www.fundsingapore.com/screener/basic_search
Year-to-date is the current 1 year return for the funds. The various ratios are also 1 year only.
## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 309841 16.6 592000 31.7 309841 16.6
## Vcells 509899 3.9 1023718 7.9 509899 3.9##
## Attaching package: 'ggvis'
##
## The following object is masked from 'package:ggplot2':
##
## resolutionequity_data <- subset(equity, select=c("ytd", "sd", "rr", "te", "sharpe"))
cor(equity_data, method="pearson")## ytd sd rr te sharpe
## ytd 1.0000000 -0.1903356 0.8194600 -0.5265297 0.7742064
## sd -0.1903356 1.0000000 -0.2095052 0.4496531 -0.1324322
## rr 0.8194600 -0.2095052 1.0000000 -0.5113336 0.9356863
## te -0.5265297 0.4496531 -0.5113336 1.0000000 -0.4203793
## sharpe 0.7742064 -0.1324322 0.9356863 -0.4203793 1.0000000bond_data <- subset(bond, select=c("ytd", "sd", "rr", "te", "sharpe"))
cor(bond_data, method="pearson")## ytd sd rr te sharpe
## ytd 1.0000000 -0.2518510 0.7132983 -0.6163540 0.4189152
## sd -0.2518510 1.0000000 -0.3484750 0.3768545 -0.1576947
## rr 0.7132983 -0.3484750 1.0000000 -0.4144627 0.5238807
## te -0.6163540 0.3768545 -0.4144627 1.0000000 -0.2676765
## sharpe 0.4189152 -0.1576947 0.5238807 -0.2676765 1.0000000mixed_assets_data <- subset(mixed_assets, select=c("ytd", "sd", "rr", "te", "sharpe"))
cor(mixed_assets_data, method="pearson")## ytd sd rr te sharpe
## ytd 1.0000000 -0.4961621 0.6025356 -0.4581520 0.5186862
## sd -0.4961621 1.0000000 -0.6896321 0.8726764 -0.3814591
## rr 0.6025356 -0.6896321 1.0000000 -0.8440010 0.6584167
## te -0.4581520 0.8726764 -0.8440010 1.0000000 -0.5595356
## sharpe 0.5186862 -0.3814591 0.6584167 -0.5595356 1.0000000#money_market_data <- subset(money_market, select=c("ytd", "sd", "rr", "te", "sharpe"))
#cor(money_market_data, method="pearson")
#real_estate_data <- subset(real_estate, select=c("ytd", "sd", "rr", "te", "sharpe"))
#cor(real_estate_data, method="pearson")RISK ADJUSTED RETURN
A concept that refines an investment’s return by measuring how much risk is involved in producing that return, which is generally expressed as a number or rating.
BREAKING DOWN ‘Risk-Adjusted Return’
There are five principal risk measures: alpha, beta, r-squared, standard deviation and the Sharpe ratio. Each risk measure is unique in how it measures risk. When comparing two or more potential investments, an investor should always compare the same risk measures to each different investment in order to get a relative performance perspective.
Read more on : http://www.investopedia.com/terms/r/riskadjustedreturn.asp#ixzz3w4fDJhp6
1. SHARPE RATIO
An indicator of whether an investment’s return is due to smart investing decisions or a result of excess risk.
Sharpe is useful, when comparing similar portfolios or instruments. There is no absolute definition of a ‘good’ or ‘bad’ Sharpe ratio, beyond the thought that a fund with a negative Sharpe would have been better off investing in risk-free government securities. But clearly the higher the Sharpe ratio the better: as the ratio increases, so does the risk-adjusted performance. In effect, when analysing similar investments, the one with the highest Sharpe has achieved more return while taking on no more risk than its fellows.
2. Tracking Error
This statistic measures the standard deviation of a fund’s excess returns over the returns of an index or benchmark portfolio. As such, it can be an indication of ‘riskiness’ in the manager’s investment style. A Tracking Error below 2 suggests a passive approach, with a close fit between the fund and its benchmark. At 3 and above the correlation is progressively looser: the manager will be deploying a more active investment style, and taking bigger positions relative to the benchmark’s composition.
3. Standard Deviation
Measures how much return on an investment is deviating from the expected normal or average returns. While volatility is specific to a fund’s particular mix of investments, and comparison to other portfolios is difficult, clearly, for those that offer similar returns, the lower-volatility funds are preferable. There is no point in taking on higher risk than necessary in order to achieve the same reward.
# Make a scatterplot of the pressure dataset
#pressure %>% ggvis(~temperature, ~pressure) %>% layer_points()
# Adapt the code you wrote for the first challenge: show bars instead of points
#pressure %>% ggvis(~temperature, ~pressure) %>% layer_bars()
# Adapt the code you wrote for the first challenge: show lines instead of points
#pressure %>% ggvis(~temperature, ~pressure) %>% layer_lines()
# Adapt the code you wrote for the first challenge: map the fill property to the temperature variable
#pressure %>% ggvis(~temperature, ~pressure, fill = ~temperature) %>% layer_points()
# Extend the code you wrote for the previous challenge: map the size property to the pressure variable
#pressure %>% ggvis(~temperature, ~pressure, fill = ~temperature, size = ~pressure) %>% layer_points()