Wednesday, September 05, 2018

August 2018 Report

The Australian Dollar fell from USD 0.7432 to USD 0.7201. The MSCI World Index rose 0.83% and the S&P 500 rose 3.26%. The ASX 200 rose 1.76%. All these are total returns including dividends. We gained 2.04% in Australian Dollar terms and -1.13% in US Dollar terms. So, we  outperformed the Australian market and underperformed international markets.

The best performing investment in dollar terms was CFS Geared Share Fund gaining AUD 9.2k  followed by Unisuper (CDM.AX) gaining AUD 6.9k and Bluesky Alternatives (BAF.AX), gaining AUD 4.5k. The best performing asset class was "real estate", gaining 2.45% followed by US stocks, gaining 2.28%. The worst performing asset class was hedge funds, gaining 0.26%.

The following is table of investment performance statistics computed over the last 60 months (extended from 36 months previously) of data:

The first two rows gives the annual rate of return and Sharpe ratio for our investment performance in US dollars and Australian dollars. The other statistics are in comparison to the two indices. Based on beta, compared to the MSCI World Index we seem to be slightly geared, while compared to the Australian index we are less sensitive to market movements. We have a slightly positive alpha compared to the Australian and a negative beta compared to world markets. Finally, we now capture more of the up movements and less of the down movements in the Australian market and the reverse in the international markets. The fall in the Australian Dollar over this period explains the poor performance compared to international benchmarks.

This month I only made a relatively small amount of money trading futures – USD 1.8k – though this is the second best performance so far in dollar terms. The table * compares my performance to the market and the model:

This month was the fifth month of the futures trading experiment. The first month was the model development phase, and since then I have been trying to get disciplined at trading and further incrementally improve the model. August was rather erratic. At one point I was up AUD 11k over the amount originally put into this trading account and then blew almost all of it in a mixture of bad model trades and bad trading in and out of positions. One of the bad model trades would now not happen, due to improvement of the model, so I have learned something from this experience. In the early part of the month I was trading two NQ contracts. After the bad trading I cut it back down to one contract again. So we are back in Stage 2 of the trading experiment, which is learning to consistently trade one contract. I am only doing long trades for the moment, due to the reduced volatility at the moment. Still, I unnecessarily sacrificed about USD 1,500 by closing a long position early. The model slightly underperformed the market this month. The same thing happened in May when the market had a very strong result. The model is bearish and under-performs when the market is strong and outperforms when the market is weak.

We reversed progress towards the new long-run asset allocation:

Total leverage includes borrowing inside leveraged (geared) mutual (managed) funds. The allocation is according to total assets including the true exposure in leveraged funds.

The deterioration in allocation, came mostly due to investment activity. We invest AUD 2k monthly in a set of managed funds, and there are also retirement contributions. Then there are distributions from funds and dividends. During the month, I also:
    • I added another AUD 10k to the Winton Global Alpha fund, but withdrew AUD 50k from the trading account, for a net reduction in the allocation to commodities.
    • I received the payment for the takeover of IPE but bought some more shares in OCP.AX, overall reducing the allocation to private equity.
    • Added to positions in PMC.AX and CDM.AX, increasing the allocation to hedge funds. 
    • I added a small position in Yellow Brick Road (YBR.AX), increasing the allocation to Australian small cap stocks.
    * The statistics at the bottom of the table are based on only 5 months of data and so are not at all reliable yet.

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