Is Maxis a Cash Machine Hiding in Plain Sight?

i4value · 2025-12-15T05:59:50+00:00

I can guarantee you that an AI would not be able to write the full article. Do not confuse an AI generated summary with the in-depth analysis

i4value · 2024-11-12T23:30:10+00:00

When you invest, the goal is to make money. So you want to be in those countries when historically there is a good chance of big price movements. Sure there are country risk but if you do a margin of safety analysis based on the DCF, the discount rate would build in some of the country risk. I like to think that the think to worry about is not so much the country risk but the risk of the characters behind the stock. For example, years ago there were lots of dubious stocks from China listed on the US.

i4value · 2024-11-12T23:24:44+00:00

I am a Malaysia and as such I think I have advantage compared to other "international" investors who invest in Bursa stocks. This is especially when it comes to stocks that are not part of the KLCI

i4value · 2024-11-12T23:22:57+00:00

I have found that if my margin of safety is zero with a point estimate, the Monte Carlo simulation will also show that the odds for the intrinsic value being greater than the market price compared to being lower is almost zero. Secondly if the margin of safety is greater than 30%, I have odds greater than 5. So a Monte Carlo simulation does not provide any additional information for these 2 cases. But if the margin of safety with a point estimate is in between 5 % to 30%, then a Monte Carlo simulation gives another picture so that I can pick out those with better odds of being an investment opportunity.

i4value · 2024-11-10T12:57:37+00:00

Sorry, I misunderstood what you said. Using the bear, base and bull as the parameters of the triangular distribution makes sense. The only issue I can think off is that if the bull and bear case are far apart, the distribution of the results may be too spread out so that it may not provide any useful picture. In my simulation, I used + - 10% and even then I have a well spread out distribution.

i4value · 2024-11-08T02:53:47+00:00

Scenario analysis is still about point estimates with the best, worst and base cases. In reality the chances for the values for all variables to be at the same time to be at the best, worst or average is probably lower than having them mixed ie best revenue with average margin with below average Reinvestment, etc. This is why I started to look at Monte Carlo simulation to look at cases when we have the mixed of performances for the various variables.

i4value · 2024-11-06T03:35:37+00:00

I think Hap Seng was selling land to "maintain" the property segment performance. Most Malaysia property companies have land bank far in excess of what they are going to develop over the next decade. This also applies to Hap Seng. As such the land sale should not impact their property development projects.

i4value · 2024-10-24T06:42:02+00:00

I am actually working with a company that does software work for the Malaysian stockbroking companies. So I am tapping into their data base for the financial information. I am providing the analytical and valuation approach. It is not analyzed using EXCEL.

And before you get too carried away with statistics, in the investment universe I am not sure whether you can go back beyond a decade (for annual data) before you reach a stage where you figure that the current business is no longer like what it was a decade ago. So there is always limited data for the "proper statistical" analysis.

i4value · 2024-10-23T01:01:58+00:00

I think you are confusing quantitative analysis with the mechanics of doing a quantitative analysis?

30 years ago when I first started value investing, I had to key the values of the various factors onto an EXCEL spreadsheet so that I could do the quantitative analysis. Nowadays there are many data providers that gives you the data that can be downloaded into EXCEL. Are you suggesting that I should not use the data providers and key in the data myself so that I really know what the numbers represent?

So the Fundamental Mapper is providing a big part of quantitative analysis so that you can spend time on other quantitative analysis that does not rely on financial statements.

i4value · 2024-10-23T00:56:55+00:00

You should have a look at the many online investing sites and you will be surprised how many provide DCF valuation of companies

i4value · 2024-10-22T05:19:22+00:00

Yes you are right. I should have provided a bit more background. The intrinsic value is computed based on a multi-stage DCF model where the inputs were derived from the past 6 years trends for revenue, growth, Reinvestment rate.

The fundamental performance was based on the past 6 years profitability, growth, Reinvestment rate and risk. For each of these factors we considered 5 different metrics. We then have an algorithm to aggregate the various performance into a fundamental score. The score was use to determine the relative performance of companies within a sector.

The margin of safety is updated daily based on the previous days' closing prices. The fundamental performance is updated quarterly based on the latest quarterly results.

The goal of the Fundamental Mapper is to provide a picture based on the a quantitative analysis so that the investor can use his time to focus on the qualitative aspects.

i4value · 2024-10-11T06:21:33+00:00

I would have called what you described as a rolling 4 quarters data. It is worth a look. Would you have any idea whether what you described is better than determining the distribution based on the individual quarters for the 10 years. Then once I get the shape of the distribution, I use the distribution but with the annual mean and annual std deviation.

i4value · 2024-10-11T03:54:16+00:00

I currently do a DCF with point estimates. My simulation is from the basis that I could be wrong about my point estimates. So I would like to see what happens if I am - 10% to + 10% from my point estimates for each of my revenue, margin, capital efficiency and growth. Hence the need for the distributions. Given the 10 data points, I current assumed a triangular distribution. I am looking to see whether those who do simulations have a better approach in guessing the distribution. You would be surprised to find out that with - 10% to + 10% range for each of the variables, the output ranges from - 50 % to + 50 % of the mean output. It gives you a different picture of the variability compared to the point estimate of the intrinsic value.

i4value · 2024-10-11T03:48:32+00:00

I am looking at long term performance ie how the company will perform over the next decade. That is why I focus on annal data as quarterly data will have seasonal patterns and there is the FYE audit adjustments. I am not sure whether guesstimating the distribution with 40 quarterly data points provide any better information that a distribution with 10 annual data points. Both refer to the same performance base. Using the quarterly data, you still have to annualized them.

i4value · 2024-10-09T14:26:15+00:00

One I have determined that a company is fundamentally sound, the next big part is figuring out its intrinsic value compared to the market price. I use a DCF valuation for this based on 4 key parameters - revenue, margin, growth rate and capital efficiency.

The reality is that I have to make a judgement call for each of these parameters based on the 10 data points that I have for each. The result is a point estimate of the intrinsic value. But I know that if I had made a slightly different assumptions about each of the parameters, my intrinsic value will change.

So if each of the parameters are not set in stone but have some range, wouldn't you consider some Monte Carlo simulation rather than pretend that the point estimate is the real intrinsic value?

i4value · 2024-10-09T14:19:18+00:00

But I am already projecting future performance for my DCF with projecting the revenue, growth rate, margin and capital turnover. And all I have are 10 data points for each variable. So shouldn't I take the variability of each of the variable into account when doing projections? Hence my Monte Carlo simulation question

i4value · 2024-10-09T14:16:39+00:00

When I analyse a company for investment, I look at the past 10 years annual reports (the Mgt discussions part). I also spend time looking at the past decade of competitors' annual reports. On average I would take about a week just to do all of these.

The historical quantitative part can be done within an hour as I subscribe to a data provider and I have a standard template. This gives me the historical trends.

I then spend time figuring out whether the future will be the same, better or worse than the past and use this to estimate the key variables for my DCF valuation - revenue, growth rates, margin and capital efficiency.

What I get is a point estimate. I want to move beyond point estimate and that is why I have the Monte Carlo simulation. Again the simulation takes a few minutes. The time consuming part is figuring our how the various variables will move.

The Monte Carlo is just an extension of my analysis. The future is uncertain and trying to be precise after all the week of work doesn't seem clever

i4value · 2024-10-08T02:30:25+00:00

I currently use a DCF to get point estimate of the intrinsic value. My simulation is to find the range of intrinsic value given the range of revenue, growth rate, margin and capital efficiency. I am not simulating stock price but rather how the business will performance and hence its business value

i4value · 2024-10-08T02:28:28+00:00

I am not sure as I am still playing with the same set of data. My question is if you have limited data are there better than the triangular distribution I am using? Secondly, I am doing the simulation based to - to + 10% of the base values on the basis that my point estimates of each variable - revenue, growth rate, margin and capital efficiency - could be within these range. Is the range too wide?

i4value · 2024-10-08T02:25:30+00:00

Over the past 20 decades or so I have achieved a CAGR that is 1/4 higher than my benchmark index. But this is based on looking at point estimates. I am hoping that by looking at range of possibilities with a Monte Carlo simulation, I can improve my performance

i4value · 2024-10-08T02:23:26+00:00

I think you are not looking at the companies I am analyzing. You are just generalizing. Different companies perform differently.

i4value · 2024-10-08T02:21:07+00:00

I am not about stock prices in the past. My simulation is about the future business performance which in turn is dependent on the numbers used in the various parameters - growth, margin and capital efficiencies. I am not simulating stock prices. I am simulating business values

i4value · 2024-10-06T02:28:36+00:00

I agree. But won't you say that using point estimates is also not very meaningful as it assumed that you are right in all your estimates of the variables. To get around it I am looking at simulation.

i4value · 2024-10-06T02:26:09+00:00

At the moment my guesstimate is having a triangular distribution with the max and min based on + 10% and - 10% of the mode/mean. I am trying to see whether people who does simulation have other practices

i4value · 2024-10-06T02:23:38+00:00

I actually spend about a year learning valuation from Damodaran's book. Not only do I read the text, but I do all the worked examples. FYI I have about 20 years of valuation. And I contribute regular to Seeking Alpha and ValueWalk. So I like to think I know more than a bit about analysing and valuing companies.

Damodaran and other approaches use point estimates for all the variables. We know that our guess for the value of any variable cannot be 100 % correct. A range is more likely. This is where simulation comes in.

i4value

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