• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.

• Journal of the Korean Wood Science and Technology
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• v.2 no.4
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• pp.4-14
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• 1974

The total forest land area in Korea accounts for some 67 percent of the nation's land total. Its productivity, however, is very low. Consequently, forest production accounts for only about 2 percent of the gross national product and a minor proportion of no more than about 5 percent versus primary industry. In this case, however, only the direct income from forestry is taken into account, making no reference to the forestry output induced by other industrial sectors. The value added Or the induced forestry output in manufacturing the primary wood products into higher quality products, makes a larger contribution to the economy than direct contribution. So, this author has tried to analyze the structure of forestry and compute the repercussion effect and the induced output of primary forest products when utilized by other industries for their raw materials, Hsing the input-output table and attached tables for 1963 and 1966 issued by the Bank of Korea. 1. Analysis of forestry structure A. Changes in total output Durng the nine-year period, 1961-1969, the real gross national product in Korea increased 2.1 times, while that of primary industries went up about 1. 4 times. Forestry which was valued at 9,380 million won in 1961, was picked up about 2. 1 times to 20, 120 million won in 1969. The rate of the forestry income in the GNP, accordingly, was no more than 1.5 percent both in 1961 and 1962, whereas its rate in primary industries increased 3.5 to 5.4 percent. Such increase in forestry income is attributable to increased forest production and rise in timber prices. The rate of forestry income, nonetheless, was on the decrease on a gradual basis. B. Changes in input coefficient The input coefficient which indicates the inputs of the forest products into other sectors were up in general in 1966 over 1963. It is noted that the input coefficient indicating the amount of forest products supplied to such industries closely related with forestry as lumber and plywood, and wood products and furniture, showed a downward trend for the period 1963-1966. On the other hand, the forest input into other sectors was generally on the increase. Meanwhile, the input coefficient representing the yolume of the forest products supplied to the forestry sector itself showed an upward tendency, which meant more and more decrease in input from other sectors. Generally speaking, in direct proportion to the higher input coefficient in any industrial sector, the reinput coefficient which denotes the use of its products by the same sector becomes higher and higher. C. Changes in ratio of intermediate input The intermediate input ratio showing the dependency on raw materials went up to 15.43 percent m 1966 from 11. 37 percent in 1963. The dependency of forestry on raw materials was no more than 15.43 percent, accounting for a high 83.57 percent of value added. If the intermediate input ratio increases in any given sector, the input coefficient which represents the fe-use of its products by the same sector becomes large. D. Changes in the ratio of intermediate demand The ratio of the intermediate demand represents the characteristics of the intermediary production in each industry, the intermediate demand ratio in forestry which accunted for 69.7 percent in 1963 went up to 75.2 percent in 1966. In other words, forestry is a remarkable industry in that there is characteristics of the intermediary production. E. Changes in import coefficient The import coefficient which denotes the relation between the production activities and imports, recorded at 4.4 percent in 1963, decreased to 2.4 percent in 1966. The ratio of import to total output is not so high. F. Changes in market composition of imported goods One of the major imported goods in the forestry sector is lumber. The import value increased by 60 percent to 667 million won in 1966 from 407 million won in 1963. The sales of imported forest products to two major outlets-lumber and plywood, and wood products and furniture-increased to 343 million won and 31 million won in 1966 from 240million won and 30 million won in 1963 respectively. On the other hand, imported goods valued at 66 million won were sold to the paper products sector in 1963; however, no supply to this sector was recorded in 1963. Besides these major markets, primary industries such as the fishery, coal and agriculture sectors purchase materials from forestry. 2. Analysis of repercussion effect on production The repercussion effect of final demand in any given sector upon the expansion of the production of other sectors was analyzed, using the inverse matrix coefficient tables attached to the the I.O. Table. A. Changes in intra-sector transaction value of inverse matrix coefficient. The intra-sector transaction value of an inverse matrix coefficient represents the extent of an induced increase in the production of self-support products of the same sector, when it is generated directly and indirectly by one unit of final demand in any given sector. The intra-sector transaction value of the forestry sector rose from 1.04 in 1963 to 1, 11 in 1966. It may well be said, therefore, that forestry induces much more self-supporting products in the production of one unit of final demand for forest products. B. Changes in column total of inverse matrix coefficient It should be noted that the column total indicates the degree of effect of the output of the corresponding and related sectors generated by one unit of final demand in each sector. No changes in the column total of the forestry sector were recorded between the 1963 and 1966 figures, both being the same 1. 19. C. Changes in difference between column total and intra-sector transaction amount. The difference between the column total and intra-sector transaction amount by sector reveals the extent of effect of output of related industrial sector induced indirectly by one unit of final demand in corresponding sector. This change in forestry dropped remarkable to 0.08 in 1966 from 0.15 in 1963. Accordingly, the effect of inducement of indirect output of other forestry-related sectors has decreased; this is a really natural phenomenon, as compared with an increasing input coefficient generated by the re-use of forest products by the forestry sector. 3. Induced output of forestry A. Forest products, wood in particular, are supplied to other industries as their raw materials, increasng their value added. In this connection the primary dependency rate on forestry for 1963 and 1966 was compared, i. e., an increase or decrease in each sector, from 7.71 percent in 1963 to 11.91 percent in 1966 in agriculture, 10.32 to 6.11 in fishery, 16.24 to 19.90 in mining, 0.76 to 0.70 in the manufacturing sector and 2.79 to 4.77 percent in the construction sector. Generally speaking, on the average the dependency on forestry during the period 1963-1966 increased from 5.92 percent to 8.03 percent. Accordingly, it may easily be known that the primary forestry output induced by primary and secondary industries increased from 16, 109 million won in 1963 to 48, 842 million won in 1966. B. The forest products are supplied to other industries as their raw materials. The products are processed further into higher quality products. thus indirectly increasing the value of the forest products. The ratio of the increased value added or the secondary dependency on forestry for 1963 and 1966 showed an increase or decrease, from 5.98 percent to 7.87 percent in agriculture, 9.06 to 5.74 in fishery, 13.56 to 15.81 in mining, 0.68 to 0.61 in the manufacturing sector and 2.71 to 4.54 in the construction sector. The average ratio in this connection increased from 4.69 percent to 5.60 percent. In the meantime, the secondary forestry output induced by primary and secondary industries rose from 12,779 million Wall in 1963 to 34,084 million won in 1966. C. The dependency of tertiary industries on forestry showed very minor ratios of 0.46 percent and 0.04 percent in 1963 and 1966 respectively. The forestry output induced by tertiary industry also decreased from 685 million won to 123 million won during the same period. D. Generally speaking, the ratio of dependency on forestry increased from 17.68 percent in 1963 to 24.28 percent in 1966 in primary industries, from 4.69 percent to 5.70 percent in secondary industries, while, as mentioned above, the ratio in the case of tertiary industry decreased from 0.46 to 0.04 percent during the period 1963-66. The mining industry reveals the heaviest rate of dependency on forestry with 29.80 percent in 1963 and 35.71 percent in 1966. As it result, the direct forestry income, valued at 8,172 million won in 1963, shot up to 22,724 million won in 1966. Its composition ratio lo the national income rose from 1.9 percent in 1963 to 2.3 per cent in 1966. If the induced outcome is taken into account, the total forestry production which was estimated at 37,744 million won in 1963 picked up to 105,773 million won in 1966, about 4.5 times its direct income. It is further noted that the ratio of the gross forestry product to the gross national product. rose significantly from 8.8 percent in 1963 to 10.7 percent in 1966. E. In computing the above mentioned ratio not taken into consideration were such intangible, indirect effects as the drought and flood prevention, check of soil run-off, watershed and land conservation, improvement of the people's recreational and emotional living, and maintenance and increase in the national health and sanitation. F. In conclusion, I would like to emphasize that the forestry sector exercices an important effect upon the national economy and that the effect of induced forestry output is greater than its direct income.