## Multiple Factor Model – Building Fundamental Factors

This is the second post in the series about Multiple Factor Models. I will build on the code presented in the prior post, Multiple Factor Model – Fundamental Data, and I will show how to build Fundamental factors described in the CSFB Alpha Factor Framework. For details of the CSFB Alpha Factor Framework please read CSFB Quantitative Research, Alpha Factor Framework on page 11, page 49 by P. N. Patel, S. Yao, R. Carlson, A. Banerji, J. Handelman.

The CSFB Alpha Factor Framework has both traditional Fundamental factors and industry relative Fundamental factors. Let’s start by getting Fundamental data that we will need to create Price/Earnings, Price/Sales, Price/Cash Flow, Dividend Yield, Price/Book factors. In the prior post, I mentioned that it takes a while to download historical fundamental data for all companies in the Dow Jones index, and I recommend saving fundamental data with save(data.fund, file=’data.fund.Rdata’) command. In the following code I will just load historical fundamental data with load(file=’data.fund.Rdata’) command instead of downloading all data again.

############################################################################### # Load Systematic Investor Toolbox (SIT) # http://systematicinvestor.wordpress.com/systematic-investor-toolbox/ ############################################################################### con = gzcon(url('http://www.systematicportfolio.com/sit.gz', 'rb')) source(con) close(con) #***************************************************************** # Find Sectors for each company in DOW 30 #****************************************************************** tickers = spl('XLY,XLP,XLE,XLF,XLV,XLI,XLB,XLK,XLU') tickers.desc = spl('ConsumerCyclicals,ConsumerStaples,Energy,Financials,HealthCare,Industrials,Materials,Technology,Utilities') sector.map = c() for(i in 1:len(tickers)) { sector.map = rbind(sector.map, cbind(sector.spdr.components(tickers[i]), tickers.desc[i]) ) } colnames(sector.map) = spl('ticker,sector') #***************************************************************** # Load historical data #****************************************************************** load.packages('quantmod') tickers = dow.jones.components() sectors = factor(sector.map[ match(tickers, sector.map[,'ticker']), 'sector']) names(sectors) = tickers # get fundamental data load(file='data.fund.Rdata') # get pricing data load(file='data.Rdata') #***************************************************************** # Combine fundamental and pricing data #****************************************************************** for(i in tickers) { fund = data.fund[[i]] fund.date = date.fund.data(fund) # Earnings per Share EPS = get.fund.data('Diluted EPS from Total Operations', fund, fund.date, is.12m.rolling=T) # Sales, exception not available for financial firms SALE = get.fund.data('total revenue', fund, fund.date, is.12m.rolling=T) # Common Shares Outstanding CSHO = get.fund.data('total common shares out', fund, fund.date) # Common Equity CEQ = get.fund.data('total equity', fund, fund.date) # Dividends DV.PS = get.fund.data('dividends paid per share', fund, fund.date, is.12m.rolling=T) # Cash Flow, exception not available for financial firms CFL = get.fund.data('net cash from operating activities', fund, fund.date, cash.flow=T, is.12m.rolling=T) # merge data[[i]] = merge(data[[i]], EPS, SALE, CSHO, CEQ, DV.PS, CFL) } bt.prep(data, align='keep.all', dates='1995::2011') #***************************************************************** # Create Factors #****************************************************************** prices = data$prices prices = bt.apply.matrix(prices, function(x) ifna.prev(x)) sectors = sectors[colnames(prices)] # create factors factors = list()

In the Dow Jones index there are 4 financial firms (AXP, BAC, JPM, TRV) and Sales and Cash Flow are not really measurable for financial firms. Please read Valuing Financial Service Firms by A. Damodaran for detailed explanation why Sales and Cash Flow are not really measurable for financial firms.

Next let’s create Traditional Value factors: Price/Earnings, Price/Sales, Price/Cash Flow, Dividend Yield, Price/Book.

#***************************************************************** # Traditional Value #****************************************************************** factors$TV = list() # Market Value - capitalization CSHO = bt.apply(data, function(x) ifna.prev(x[, 'CSHO'])) MKVAL = prices * CSHO # Price / Earnings EPS = bt.apply(data, function(x) ifna.prev(x[, 'EPS'])) factors$TV$EP = EPS / prices # Price / Trailing Sales SALE = bt.apply(data, function(x) ifna.prev(x[, 'SALE'])) factors$TV$SP = SALE / MKVAL # Price / Trailing Cash Flow CFL = bt.apply(data, function(x) ifna.prev(x[, 'CFL'])) factors$TV$CFP = CFL / MKVAL # Dividend Yield DV.PS = bt.apply(data, function(x) ifna.prev(x[, 'DV.PS'])) factors$TV$DY = DV.PS / prices # Price / Book Value CEQ = bt.apply(data, function(x) ifna.prev(x[, 'CEQ'])) factors$TV$BP = CEQ / MKVAL # Eliminate Price/Sales and Price/Cash Flow for financial firms factors$TV$SP[, sectors == 'Financials'] = NA factors$TV$CFP[, sectors == 'Financials'] = NA #***************************************************************** # Convert to monthly #****************************************************************** # find month ends month.ends = endpoints(prices, 'months') prices = prices[month.ends,] n = ncol(prices) nperiods = nrow(prices) ret = prices / mlag(prices) - 1 next.month.ret = mlag(ret, -1) MKVAL = MKVAL[month.ends,] for(j in 1:len(factors)) { for(i in 1:len(factors[[j]])) { factors[[j]][[i]] = factors[[j]][[i]][month.ends,] } }

To create an overall Traditional Value factor, let’s first normalize (convert to z scores) all Traditional Value factors by subtracting capitalization weighted average and dividing by standard deviation. The overall Traditional Value factor is an average of all normalized Traditional Value factors.

#***************************************************************** # Create the overall Traditional Value factor #****************************************************************** # check missing data for financial firms sapply(factors$TV, count) # normalize (convert to z scores) cross sectionaly all Traditional Value factors for(i in names(factors$TV)) { factors$TV[[i]] = (factors$TV[[i]] - cap.weighted.mean(factors$TV[[i]], MKVAL)) / apply(factors$TV[[i]], 1, sd, na.rm=T) } # compute the overall Traditional Value factor load.packages('abind') temp = abind(factors$TV, along = 3) factors$TV$AVG = factors$TV[[1]] factors$TV$AVG[] = apply(temp, c(1,2), mean, na.rm=T) # plot quintile charts for all Traditional Value factors layout(matrix(1:6,nc=2)) sapply(1:len(factors$TV), function(i) compute.quantiles(factors$TV[[i]], next.month.ret, paste(names(factors$TV)[i], 'Traditional Value')) )

I created a compute.quantiles() function in factor.model.r at github to compute and plot quantiles. For example, the quantiles chart for EP factor shows the average next month performance of stocks in each quantiles. The quantiles are created each month by ranking stocks by EP factor and grouping them into 5 quantiles. There is tendency of quantile 5 (Q5) to outperform quantile 1 (Q1) in most cases. The relationship between quantiles is not perfect, but the spread between Q5-Q1 is positive.

Next let’s examine quantiles for the overall Traditional Value factor in more details.

#***************************************************************** # Backtest quantiles and quantile spread #****************************************************************** out = compute.quantiles(factors$TV$AVG, next.month.ret, plot=F) prices = data$prices prices = bt.apply.matrix(prices, function(x) ifna.prev(x)) # create strategies that invest in each qutile models = list() for(i in 1:5) { data$weight[] = NA data$weight[month.ends,] = iif(out$quantiles == i, out$weights, 0) capital = 100000 data$weight[] = (capital / prices) * (data$weight) models[[paste('Q',i,sep='')]] = bt.run(data, type='share', capital=capital) } # spread data$weight[] = NA data$weight[month.ends,] = iif(out$quantiles == 5, out$weights, iif(out$quantiles == 1, -out$weights, 0)) capital = 100000 data$weight[] = (capital / prices) * (data$weight) models$Q5_Q1 = bt.run(data, type='share', capital=capital) #***************************************************************** # Create Report #****************************************************************** plotbt(models, plotX = T, log = 'y', LeftMargin = 3) mtext('Cumulative Performance', side = 2, line = 1)

The quantile spread Q5-Q1 shows consistent positive performance after 2000, but is inverted from 1995 to 2000. This is a bit strange and calls for more investigation.

In the next posts, I will show how to run pooled cross sectional regression to create alpha scores.

To view the complete source code for this example, please have a look at the fm.fund.factor.test() function in factor.model.test.r at github.

Thanks for this, looking great and eagerly awaiting your next post :)

In my own research, I’ve found using a rolling linear model (e.g. every 30 days) to predict the optimal weights to be nice and adative for trading purposes. You might find a similar approach valuable in deciding what to trade in (would automatically handle anomalities where stocks with bad fundamentals outperform stock with good fundamentals; etc).

Thinking about the odd behavioural change before/after 2000 one possible explanation might be that during a bear market people may flock to more solid fundamentals while during a bull market people may not care as much about the fundamentals. The dotcom bubble may also be at play here, as a lot of ‘internet’ companies had very bad fundamentals as investors flocked to them in favour of firms with stronger fundamentals. Just my speculation.