ANALISIS PERBEDAAN HEDGING KAKAO FUTURES DENGAN CROSS HEDGING KOPI ROBUSTA FUTURES YANG DIPERDAGANGAN DI BURSA BERJANGKA JAKARTA PERIODE: 2012-2016

Penelitian ini dilakukan untuk menganalisis perbedaan antara hedging kakao futures dengan cross hedging kopi robusta futures dalam meminimalkan risiko di pasar fisik komoditi kakao dengan membandingkan nilai varians return yang dihasilkan dari kedua kontrak futures. Data yang digunakan pada penelitian ini adalah data harian kakao spot, kakao futures, dan kopi robusta futures yang diperdagangkan di Bursa Berjangka Jakarta (BBJ) pada periode 2012-2016. Alat analisis yang digunakan adalah uji korelasi pearson yang bertujuan untuk menguji hubungan antara harga spot dengan futures pada saat melakukan hedging ataupun cross hedging; uji akar unit digunakan untuk melihat kestasioneritas data sebelum dilakukan uji beda; uji regresi sederhana digunakan untuk menghitung nilai ratio hedged dan ratio cross hedged; uji independent sample t-test digunakan untuk membandingkan nilai varians return yang dihasilkan dari kedua kontrak futures tersebut. Hasil analisis menunjukkan bahwa tidak terdapat perbedaan varian returns yang dihasilkan dari keduanya. Dalam hal ini, penanganan risiko pada komoditi kakao di pasar fisiknya pada saat melakukan hedging ataupun cross hedging memiliki tingkat risiko yang sama, sehingga hedging kakao futures dan cross hedging kopi robusta futures sama-sama dapat digunakan untuk meminimalkan risiko pada pasar fisik kakao

Problems in Learning under Limited Resources and Information

The main theme of this thesis is to investigate how learning problems can be solved in the face of limited resources and with limited information to base inferences on. We study feature-efficient prediction when each feature comes with a cost and our goal is to construct a good predictor during training time with total cost not exceeding the given budget constraints. We also study complexity-theoretic properties of models for recovering social networks with knowledge only about how people in the network vote or how information propagates through the network

The amplitudes of FRN and P300.

<p>The FRN amplitudes (mean ± SE, in µV) for the eight experimental conditions were shown in (A). The difference in FRN amplitude between losses (in red) and wins (in blue) was larger when the price was cheap versus expensive, suggesting that the FRN is sensitive to the true value of money (B). The FRN effect (losses minus wins) was similar between large magnitude in expensive context and small magnitude in cheap context (C). The P300 amplitudes (mean ± SE, in µV) for the eight conditions were shown in (D). No effect of true value (E) and face value (F) on P300 was found. S: small magnitude; L: large magnitude; All: across small and large magnitude. ^* p<0.005.</p

The ERP grand-average waveforms.

<p>(A) Grand-average waveforms at channel Fz and Pz for conditions that differed in real value but were identical in face value of money. (B) Grand-average waveforms at channel Fz and Pz for conditions that differed in face value but were identical in real value of money. Cheap_Loss: losing in cheap price context across magnitude; Cheap_Win: winning in cheap price context across magnitude; Expensive_Loss: losing in expensive price context across magnitude; Expensive_Win: winning in expensive context across magnitude; Cheap_Small_Loss: losing the small magnitude in cheap condition; Cheap_Small_Win: winning the small magnitude in cheap condition; Expensive_Small_Loss: losing the large magnitude in expensive condition; Expensive_Small_Win: winning the large magnitude in expensive condition.</p

The money illusion effect for each participant.

<p>The behavioral money illusion effect for 19 participants (numerically ordered). The y axis represents the differences in satisfaction (difference between win and loss for large magnitude in expensive context - difference between win and loss for small magnitude in cheap context).</p

Post-experiment subjective ratings of feeling.

<p>The self-reporting satisfaction scores (mean ± SE) for the eight experimental conditions were shown in (A). The difference in satisfaction between losses (in red) and wins (in blue) was larger when the price was cheap versus expensive (B). The difference in satisfaction between losses and wins was larger for large magnitude in expensive context than for small magnitude in cheap context, showing money illusion (C). The self-reported surprise scores (mean ± SE) for the eight conditions were shown in (D). The experienced surprise was not modulated by the true value of money (E) or the face value of money (F). S: small magnitude; L: large magnitude; All: across small and large magnitude. ^* p<0.05, ^** p<0.001.</p

The difference waveforms and topographical maps.

<p>(A) Difference waveform (loss-win) and maps of cheap condition. (B) Difference waveform (loss-win) and topographical maps in expensive condition. (C) Difference waveform (loss-win) and topographical maps in small magnitude and cheap condition. (D) Difference waveform (loss-win) and topographical maps in large magnitude and expensive condition.</p

Experimental task design.

<p>At the beginning of each trial, the cheap price or the expensive price context information was shown. Then participants performed a simple gambling game in which they win or lose money on the basis of unpredictable outcomes. Participants chose one gambling card from the two and received winning or losing feedback.</p

sj-docx-1-jtr-10.1177_00472875231200494 – Supplemental material for When Essence is Lost: The Consequences of Commercialization in Historical Towns

Supplemental material, sj-docx-1-jtr-10.1177_00472875231200494 for When Essence is Lost: The Consequences of Commercialization in Historical Towns by Jiaying Lyu, Yi Huang and Lili Wang in Journal of Travel Research</p

Characterization of the chloroplast genome of the marine microalga <i>Tetraselmis marina</i> (Cienkowski) R.E.Norris, Hori & Chihara 1980

Tetraselmis marina (Cienkowski) R.E.Norris, Hori & Chihara 1980, a costal green microalga, is considered as a promising animal feed in aquaculture due to the high content of fatty acids and carotenoid. Furthermore, T. marina plays important roles in bioremediation. In this study, we assembled the complete chloroplast genome of T. marina. Results showed that the full length of the complete chloroplast genome was 96,151 bp, containing a large single-copy region of 62,574 bp, a small single-copy region of 1261 bp, and a pair of inverted repeat regions of 16,158 bp. The GC content of the genome was 36.6%. A total of 125 genes were annotated, including 81 protein coding genes, 38 tRNA genes, and six rRNA genes. Phylogenetic analysis based on 22 chloroplast genomes suggested that T. marina was closely related to Tetraselmis sp. CCMP 881.</p